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Journal articles on the topic 'Non-overlapping cameras'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Yin, Lei, Xiangjun Wang, Yubo Ni, Kai Zhou, and Jilong Zhang. "Extrinsic Parameters Calibration Method of Cameras with Non-Overlapping Fields of View in Airborne Remote Sensing." Remote Sensing 10, no. 8 (August 16, 2018): 1298. http://dx.doi.org/10.3390/rs10081298.

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Multi-camera systems are widely used in the fields of airborne remote sensing and unmanned aerial vehicle imaging. The measurement precision of these systems depends on the accuracy of the extrinsic parameters. Therefore, it is important to accurately calibrate the extrinsic parameters between the onboard cameras. Unlike conventional multi-camera calibration methods with a common field of view (FOV), multi-camera calibration without overlapping FOVs has certain difficulties. In this paper, we propose a calibration method for a multi-camera system without common FOVs, which is used on aero photogrammetry. First, the extrinsic parameters of any two cameras in a multi-camera system is calibrated, and the extrinsic matrix is optimized by the re-projection error. Then, the extrinsic parameters of each camera are unified to the system reference coordinate system by using the global optimization method. A simulation experiment and a physical verification experiment are designed for the theoretical arithmetic. The experimental results show that this method is operable. The rotation error angle of the camera’s extrinsic parameters is less than 0.001rad and the translation error is less than 0.08 mm.

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Van Crombrugge, Izaak, Rudi Penne, and Steve Vanlanduit. "Extrinsic camera calibration for non-overlapping cameras with Gray code projection." Optics and Lasers in Engineering 134 (November 2020): 106305. http://dx.doi.org/10.1016/j.optlaseng.2020.106305.

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3

Lv, Rui Peng, Hai Gang Sui, Ji Hui Tu, Xiao Yu Cai, and Liang Dong. "Object Tracking across Non-Overlapping Cameras Based on Improved TLD and Multi-Feathers Object Matching." Applied Mechanics and Materials 602-605 (August 2014): 1713–17. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1713.

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Object tracking across non-overlapping views is a hot and important research topic in compute vision. In this paper, a novel method to track the interested object continuously across non-overlapping cameras is presented. This challenging task is taken as two sub-problems: single camera object tracking and object matching across disjoint cameras. An object tracking algorithm which improves Tracking-Learning-Detection (TLD) algorithm by adding background extraction and Kalman filter is presented to deal with the first problem. A new object matching algorithm based on the fusion of global features and local features at the assistance of 3D GIS is also introduced for object matching across disjoint cameras. The proposed approach does not need a training phase and inter-camera calibration. Experiments are carried out on real world videos to validate the proposed approach.

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Hanel, A., and U. Stilla. "STRUCTURE-FROM-MOTION FOR CALIBRATION OF A VEHICLE CAMERA SYSTEM WITH NON-OVERLAPPING FIELDS-OF-VIEW IN AN URBAN ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-1/W1 (May 31, 2017): 181–88. http://dx.doi.org/10.5194/isprs-archives-xlii-1-w1-181-2017.

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Vehicle environment cameras observing traffic participants in the area around a car and interior cameras observing the car driver are important data sources for driver intention recognition algorithms. To combine information from both camera groups, a camera system calibration can be performed. Typically, there is no overlapping field-of-view between environment and interior cameras. Often no marked reference points are available in environments, which are a large enough to cover a car for the system calibration. In this contribution, a calibration method for a vehicle camera system with non-overlapping camera groups in an urban environment is described. A-priori images of an urban calibration environment taken with an external camera are processed with the structure-frommotion method to obtain an environment point cloud. Images of the vehicle interior, taken also with an external camera, are processed to obtain an interior point cloud. Both point clouds are tied to each other with images of both image sets showing the same real-world objects. The point clouds are transformed into a self-defined vehicle coordinate system describing the vehicle movement. On demand, videos can be recorded with the vehicle cameras in a calibration drive. Poses of vehicle environment cameras and interior cameras are estimated separately using ground control points from the respective point cloud. All poses of a vehicle camera estimated for different video frames are optimized in a bundle adjustment. In an experiment, a point cloud is created from images of an underground car park, as well as a point cloud of the interior of a Volkswagen test car is created. Videos of two environment and one interior cameras are recorded. Results show, that the vehicle camera poses are estimated successfully especially when the car is not moving. Position standard deviations in the centimeter range can be achieved for all vehicle cameras. Relative distances between the vehicle cameras deviate between one and ten centimeters from tachymeter reference measurements.

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LIU, Shao-hua, Mao-jun ZHANG, and Wang CHEN. "Data association algorithm of multiple non-overlapping cameras." Journal of Computer Applications 29, no. 9 (November 13, 2009): 2378–82. http://dx.doi.org/10.3724/sp.j.1087.2009.02378.

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6

Cheng, De, Yihong Gong, Jinjun Wang, Qiqi Hou, and Nanning Zheng. "Part-aware trajectories association across non-overlapping uncalibrated cameras." Neurocomputing 230 (March 2017): 30–39. http://dx.doi.org/10.1016/j.neucom.2016.11.038.

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7

Lee, Young-Gun, Zheng Tang, and Jenq-Neng Hwang. "Online-Learning-Based Human Tracking Across Non-Overlapping Cameras." IEEE Transactions on Circuits and Systems for Video Technology 28, no. 10 (October 2018): 2870–83. http://dx.doi.org/10.1109/tcsvt.2017.2707399.

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8

Ukita, Norimichi, Yusuke Moriguchi, and Norihiro Hagita. "People re-identification across non-overlapping cameras using group features." Computer Vision and Image Understanding 144 (March 2016): 228–36. http://dx.doi.org/10.1016/j.cviu.2015.06.011.

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9

Xia, Renbo, Maobang Hu, Jibin Zhao, Songlin Chen, Yueling Chen, and ShengPeng Fu. "Global calibration of non-overlapping cameras: State of the art." Optik 158 (April 2018): 951–61. http://dx.doi.org/10.1016/j.ijleo.2017.12.159.

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10

KAWASAKI, Atsushi, Kosuke HARA, and Hideo SAITO. "Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment." IEICE Transactions on Information and Systems E101.D, no. 5 (May 1, 2018): 1232–42. http://dx.doi.org/10.1587/transinf.2017mvp0006.

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11

HUANG Xin, 黄新, 黄柏华 HUANG Bai-hua, and 熊显名 XIONG Xian-ming. "Target handoff based on improved ORB algorithm on non-overlapping cameras." Chinese Journal of Liquid Crystals and Displays 31, no. 8 (2016): 810–17. http://dx.doi.org/10.3788/yjyxs20163108.0810.

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Yu, Chunyan, and Shijun Zhong. "Discriminative Feature Learning for Pedestrian Instant Alignment across Non-Overlapping Cameras." Journal of Computer-Aided Design & Computer Graphics 31, no. 4 (2019): 602. http://dx.doi.org/10.3724/sp.j.1089.2019.17273.

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13

Liu, Cong, Xiaoyuan He, Xiaopeng Liu, and Zhihong Xu. "High-precision and flexible calibration method of non-overlapping array cameras." Applied Optics 58, no. 33 (November 20, 2019): 9251. http://dx.doi.org/10.1364/ao.58.009251.

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14

Torres, Juan, Henri Bouma, Jose-Manuel Menéndez, and Klamer Schutte. "Linear colour correction for multiple illumination changes and non-overlapping cameras." IET Image Processing 9, no. 4 (April 1, 2015): 280–89. http://dx.doi.org/10.1049/iet-ipr.2014.0149.

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15

Hellwig, S., and N. Treutner. "ROBUST PERSON TRACKING WITH MULTIPLE NON-OVERLAPPING CAMERAS IN AN OUTDOOR ENVIRONMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XXXIX-B5 (July 28, 2012): 339–44. http://dx.doi.org/10.5194/isprsarchives-xxxix-b5-339-2012.

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Li, Zaijuan, and Volker Willert. "Subtleties of extrinsic calibration of cameras with non-overlapping fields of view." tm - Technisches Messen 86, no. 7-8 (July 26, 2019): 433–42. http://dx.doi.org/10.1515/teme-2019-0030.

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AbstractThe calibration of the relative pose between rigidly connected cameras with non-overlapping fields of view (FOV) is a prerequisite for many applications. In this paper, the subtleties of the experimental realization of such calibration optimization methods like in (Z. Liu, et al., Measurement Science and Technology, 2011, Z. Li, V. Willert, Intelligent Transportation Systems (ITSC), 2018) are presented. Two strategies that could be adapted to certain optimization processes to find better local minima are evaluated. The first strategy is a careful measurement acquisition of pose pairs for solving the calibration problem, which improves the accuracy of the initial value for the following non-linear refinement. The second strategy is the introduction of a quality measure for the image data used for the calibration, which is based on the projection size of the known planar calibration patterns on the image. We show that introducing an additional weighting to the optimization objective chosen as a function of that quality measure improves calibration accuracy and increases robustness against noise. The above strategies are integrated into different setups and their improvement is demonstrated both in simulation and real-world experiment.

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Ma Mashuang, 马玛双, 杨小冈 Yang Xiaogang, 李传祥 Li Chuanxiang, 齐乃新 Qi Naixin, and 宋平 Song Ping. "Accurate Calibration Method for Non-Overlapping Fields of Cameras Based on Spatial Constraints." Acta Optica Sinica 39, no. 10 (2019): 1015003. http://dx.doi.org/10.3788/aos201939.1015003.

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18

Tan, Feigang, Weiming Liu, Ling Huang, Cong Zhai, Wei Shi, and Yanshan Li. "Person Re-identification Across Multiple Non-overlapping Cameras by Grouping Similarity Comparison Model." Chinese Journal of Electronics 26, no. 5 (September 1, 2017): 905–11. http://dx.doi.org/10.1049/cje.2016.08.007.

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19

Narayan, Neeti, Nishant Sankaran, Srirangaraj Setlur, and Venu Govindaraju. "Learning deep features for online person tracking using non-overlapping cameras: A survey." Image and Vision Computing 89 (September 2019): 222–35. http://dx.doi.org/10.1016/j.imavis.2019.07.007.

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20

Xu, Yongjia, Feng Gao, Zonghua Zhang, and Xiangqian Jiang. "A calibration method for non-overlapping cameras based on mirrored absolute phase target." International Journal of Advanced Manufacturing Technology 104, no. 1-4 (March 9, 2018): 9–15. http://dx.doi.org/10.1007/s00170-018-1704-8.

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21

Guan, Banglei, Linshen Yao, Haibo Liu, and Yang Shang. "An accurate calibration method for non-overlapping cameras with double-sided telecentric lenses." Optik 131 (February 2017): 724–32. http://dx.doi.org/10.1016/j.ijleo.2016.11.156.

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22

Tesfaye, Yonatan Tariku, Eyasu Zemene, Andrea Prati, Marcello Pelillo, and Mubarak Shah. "Multi-target Tracking in Multiple Non-overlapping Cameras Using Fast-Constrained Dominant Sets." International Journal of Computer Vision 127, no. 9 (May 13, 2019): 1303–20. http://dx.doi.org/10.1007/s11263-019-01180-6.

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23

LIU, Shao-Hua, Shi-Ming LAI, and Mao-Jun ZHANG. "A Min-cost Flow Based Algorithm for Objects Association of Multiple Non-overlapping Cameras." Acta Automatica Sinica 36, no. 10 (December 21, 2010): 1484–88. http://dx.doi.org/10.3724/sp.j.1004.2010.01484.

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24

Hu, Hai-Miao, Wen Fang, Bo Li, and Qi Tian. "An Adaptive Multi-Projection Metric Learning for Person Re-Identification Across Non-Overlapping Cameras." IEEE Transactions on Circuits and Systems for Video Technology 29, no. 9 (September 2019): 2809–21. http://dx.doi.org/10.1109/tcsvt.2018.2869898.

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Kuan-Wen Chen, Chih-Chuan Lai, Pei-Jyun Lee, Chu-Song Chen, and Yi-Ping Hung. "Adaptive Learning for Target Tracking and True Linking Discovering Across Multiple Non-Overlapping Cameras." IEEE Transactions on Multimedia 13, no. 4 (August 2011): 625–38. http://dx.doi.org/10.1109/tmm.2011.2131639.

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26

Motz, M., G. Kemper, and D. Ciobanu. "ACCURACY VALIDATION OF TILTED CAMERA SETUPS IN OPEN SKIES PROJECT AND MAPPING APPLICATIONS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2021 (June 28, 2021): 77–83. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2021-77-2021.

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Abstract. Signed March 24, 1992, the Open Skies Treaty permits each state-party to conduct short-notice, unarmed, reconnaissance flights over the others' entire territories to collect data on military forces and activities. Observation aircraft equipped with sensors shall enable the observing party to identify significant military equipment. The Open Skies Treaty agreed on an observation of 30cm GSD. Based on 8 mid-format cameras for 3 flight levels, a system was adjusted to comply with the regulations. However, the overall aim was to also use the system for mapping purposes in Romania, specifically the medium altitude configuration. From this medium altitude configuration, one specific combination raised our interest. The designed wide corridor mapping system, using two RGB tilted cameras and one RGB nadir camera generates a certain range of resolution of the sideward looking tilted cameras and a non-homogenous distribution of the GSD in the overlapping areas. While a reduction of the GSD in the remote parts of the tilted cameras is a well-known and accepted fact, the effect in the overlap of the tilted cameras with the nadir one is the opposite mathematically. In some cases, such an effect can cause a better GSD in these areas than expected.

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Hao, Chen. "Person Re-Identification via Locally Biased Metric Learning." Applied Mechanics and Materials 687-691 (November 2014): 3932–35. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3932.

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Person re-identification, which means matching person across non-overlapping cameras in a surveillance camera network, has attracted more and more attention. A lot of metric learning based methods, which generally learn a new distance function under two pair-wise constrains, i.e. similar constrain and dissimilar constrain, were proposed to address the challenging problem due to significant appearance variances caused by pose changes, lighting variations and image resolution differences. However, these methods attempt to satisfy all similar constrains and dissimilar constrains, which may be conflict and cannot be simultaneously satisfied in the practical application. In this paper, we propose a new local metric learning method based KISS metric learning. Comparative experiments conducted on three public standard datasets have shown the promising prospect of the proposed method.

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KAWASAKI, Atsushi, Hideo SAITO, and Kosuke HARA. "Motion Estimation for Non-overlapping Cameras by Improvement of Feature Points Matching based on Urban 3D structure." Journal of the Japan Society for Precision Engineering 81, no. 12 (2015): 1146–53. http://dx.doi.org/10.2493/jjspe.81.1146.

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29

Xia, Renbo, Maobang Hu, Jibin Zhao, Songlin Chen, and Yueling Chen. "Global calibration of multi-cameras with non-overlapping fields of view based on photogrammetry and reconfigurable target." Measurement Science and Technology 29, no. 6 (April 17, 2018): 065005. http://dx.doi.org/10.1088/1361-6501/aab028.

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30

Lin, Daw-Tung, and Kuan-Yu Wu. "Wide Area Object Tracking using Multiple Cameras with Mixed Configuration of Over-lapping and Non-Overlapping FOV." International Journal of Multimedia and Ubiquitous Engineering 11, no. 3 (March 31, 2016): 239–48. http://dx.doi.org/10.14257/ijmue.2016.11.3.23.

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31

Kim, Dae Sung, and Sang Chul Shin. "Decision on the Optimal Photographing Angle and Overlapping Ratio of Non-metric Cameras for Development of Automatic Image Stitching System." Journal of Korean Society for Geospatial Information System 21, no. 2 (June 30, 2013): 117–23. http://dx.doi.org/10.7319/kogsis.2013.21.2.117.

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32

Jung, Soyi, Joongheon Kim, and Jae-Hyun Kim. "Joint Message-Passing and Convex Optimization Framework for Energy-Efficient Surveillance UAV Scheduling." Electronics 9, no. 9 (September 9, 2020): 1475. http://dx.doi.org/10.3390/electronics9091475.

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In modern surveillance systems, the use of unmanned aerial vehicles (UAVs) has been actively discussed in order to extend target monitoring areas, even for an extreme circumstances. This paper proposes an energy-efficient UAV-based surveillance system that operates from two different sequential methods. First, the proposed algorithm pursues energy-efficient operations by deactivating selected surveillance cameras on the UAVs located in overlapping areas. For this objective, a message-passing based algorithm is used because the overlapping situations can be formulated using a max-weight independent set. Next, the unscheduled UAVs based on the message-passing fly to the charging towers to be charged. This algorithm computes the optimal matching between the UAVs and charging towers and the amount of energy allocation for the scheduled UAV-tower pairs. This joint optimization is initially formulated as non-convex, and it is then reformulated to be convex, which can guarantee optimal solutions. The proposed framework achieves the desired performance, as presented in the performance evaluation.

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Marciniak, Tomasz, Zbigniew Lutowski, Sławomir Bujnowski, Dariusz Boroński, and Piotr Czajka. "Dual-Band Experimental System For Subsurface Cracks Testing." Materials Science Forum 726 (August 2012): 222–25. http://dx.doi.org/10.4028/www.scientific.net/msf.726.222.

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The authors of this article have presented a hybrid method for inspection in the visible and infrared bands. A model of a test stand equipped with a monochromatic CCD and thermovision cameras which enable execution of tests in the field of active and passive thermography has been presented. Application of two vision channels provides the possibility of observing the defects caused by cracks and non-uniformity of a material structure, in the subsurface layer. An analysis of experimental tests performed on selected objects has been presented. Images from both channels, effects of the image overlapping and profiled charts along characteristic lines in thermograms have been shown.

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Skipper, Ben, Blake Grisham, Maria Kalyvaki, Kathleen McGaughey, Krista Mougey, Laura Navarrete, Renée Rondeau, Clint Boal, and Gad Perry. "Non-overlapping Distributions of Feral Sheep (Ovis aries) and Stout Iguanas (Cyclura pinguis) on Guana Island, British Virgin Islands." Reptiles & Amphibians 20, no. 1 (March 1, 2012): 7–15. http://dx.doi.org/10.17161/randa.v20i1.13930.

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Stout Iguanas (Cyclura pinguis) remain one of the most critically endangered reptiles in the world. Factors contributing to that status include habitat loss, predation by introduced species, and competition with introduced herbivores. On Guana Island, British Virgin Islands, the presence of feral sheep (Ovis aries) has been a hypothesized detriment to iguanas. Using motion sensitive cameras, we documented the distribution of feral sheep on Guana Island in 2010. We also quantified the impact of feral sheep on ground vegetation by comparing plant abundance at long term sheep exclosures and areas where sheep were absent to areas where sheep were present. Finally, we compared sheep distribution to iguana distribution on the island. The co-occurrence of sheep and Stout Iguanas was less than expected, indicating possible competition. Although we detected no difference in vegetative cover between areas where sheep were present and absent, the long-term exclosures showed that the exclusion of sheep allowed the abundance of many plant species to increase. Our data support the hypothesis that feral sheep are altering the abundance of ground-level vegetation and limiting iguana distribution on the island.

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Zhang, Yingying, Qiaoyong Zhong, Liang Ma, Di Xie, and Shiliang Pu. "Learning Incremental Triplet Margin for Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9243–50. http://dx.doi.org/10.1609/aaai.v33i01.33019243.

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Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.

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36

Li, Yuanyuan, Sixin Chen, Guanqiu Qi, Zhiqin Zhu, Matthew Haner, and Ruihua Cai. "A GAN-Based Self-Training Framework for Unsupervised Domain Adaptive Person Re-Identification." Journal of Imaging 7, no. 4 (March 25, 2021): 62. http://dx.doi.org/10.3390/jimaging7040062.

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As a crucial task in surveillance and security, person re-identification (re-ID) aims to identify the targeted pedestrians across multiple images captured by non-overlapping cameras. However, existing person re-ID solutions have two main challenges: the lack of pedestrian identification labels in the captured images, and domain shift issue between different domains. A generative adversarial networks (GAN)-based self-training framework with progressive augmentation (SPA) is proposed to obtain the robust features of the unlabeled data from the target domain, according to the preknowledge of the labeled data from the source domain. Specifically, the proposed framework consists of two stages: the style transfer stage (STrans), and self-training stage (STrain). First, the targeted data is complemented by a camera style transfer algorithm in the STrans stage, in which CycleGAN and Siamese Network are integrated to preserve the unsupervised self-similarity (the similarity of the same image between before and after transformation) and domain dissimilarity (the dissimilarity between a transferred source image and the targeted image). Second, clustering and classification are alternately applied to enhance the model performance progressively in the STrain stage, in which both global and local features of the target-domain images are obtained. Compared with the state-of-the-art methods, the proposed method achieves the competitive accuracy on two existing datasets.

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Al-Hamad, A., A. Moussa, and N. El-Sheimy. "Video-based Mobile Mapping System Using Smartphones." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 13–18. http://dx.doi.org/10.5194/isprsarchives-xl-1-13-2014.

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The last two decades have witnessed a huge growth in the demand for geo-spatial data. This demand has encouraged researchers around the world to develop new algorithms and design new mapping systems in order to obtain reliable sources for geo-spatial data. Mobile Mapping Systems (MMS) are one of the main sources for mapping and Geographic Information Systems (GIS) data. MMS integrate various remote sensing sensors, such as cameras and LiDAR, along with navigation sensors to provide the 3D coordinates of points of interest from moving platform (e.g. cars, air planes, etc.). Although MMS can provide accurate mapping solution for different GIS applications, the cost of these systems is not affordable for many users and only large scale companies and institutions can benefits from MMS systems. <br><br> The main objective of this paper is to propose a new low cost MMS with reasonable accuracy using the available sensors in smartphones and its video camera. Using the smartphone video camera, instead of capturing individual images, makes the system easier to be used by non-professional users since the system will automatically extract the highly overlapping frames out of the video without the user intervention. Results of the proposed system are presented which demonstrate the effect of the number of the used images in mapping solution. In addition, the accuracy of the mapping results obtained from capturing a video is compared to the same results obtained from using separate captured images instead of video.

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Han, Zhisong, Yaling Liang, Zengqun Chen, and Zhiheng Zhou. "A two-stream network with joint spatial-temporal distance for video-based person re-identification." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3769–81. http://dx.doi.org/10.3233/jifs-192067.

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Video-based person re-identification aims to match videos of pedestrians captured by non-overlapping cameras. Video provides spatial information and temporal information. However, most existing methods do not combine these two types of information well and ignore that they are of different importance in most cases. To address the above issues, we propose a two-stream network with a joint distance metric for measuring the similarity of two videos. The proposed two-stream network has several appealing properties. First, the spatial stream focuses on multiple parts of a person and outputs robust local spatial features. Second, a lightweight and effective temporal information extraction block is introduced in video-based person re-identification. In the inference stage, the distance of two videos is measured by the weighted sum of spatial distance and temporal distance. We conduct extensive experiments on four public datasets, i.e., MARS, PRID2011, iLIDS-VID and DukeMTMC-VideoReID to show that our proposed approach outperforms existing methods in video-based person re-ID.

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Jurado, J. M., J. L. Cárdenas, C. J. Ogayar, L. Ortega, and F. R. Feito. "Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features." Sensors 20, no. 8 (April 15, 2020): 2244. http://dx.doi.org/10.3390/s20082244.

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The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

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Gerke, M., F. Nex, F. Remondino, K. Jacobsen, J. Kremer, W. Karel, H. Hu, and W. Ostrowski. "ORIENTATION OF OBLIQUE AIRBORNE IMAGE SETS – EXPERIENCES FROM THE ISPRS/EUROSDR BENCHMARK ON MULTI-PLATFORM PHOTOGRAMMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 185–91. http://dx.doi.org/10.5194/isprsarchives-xli-b1-185-2016.

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During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto objects is clearly a non-solved research problem so far. In our experiments we also confirm that the obtainable height accuracy is better when all images are used in bundle block adjustment. This was also shown in other research before and is confirmed here. Not surprisingly, the large overlap of 80/80% provides much better object space accuracy – random errors seem to be about 2-3fold smaller compared to the 60/60% overlap. A comparison of different software approaches shows that newly emerged commercial packages, initially intended to work with small frame image blocks, do perform very well.

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41

Gerke, M., F. Nex, F. Remondino, K. Jacobsen, J. Kremer, W. Karel, H. Hu, and W. Ostrowski. "ORIENTATION OF OBLIQUE AIRBORNE IMAGE SETS – EXPERIENCES FROM THE ISPRS/EUROSDR BENCHMARK ON MULTI-PLATFORM PHOTOGRAMMETRY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 3, 2016): 185–91. http://dx.doi.org/10.5194/isprs-archives-xli-b1-185-2016.

Full text

Abstract:

During the last decade the use of airborne multi camera systems increased significantly. The development in digital camera technology allows mounting several mid- or small-format cameras efficiently onto one platform and thus enables image capture under different angles. Those oblique images turn out to be interesting for a number of applications since lateral parts of elevated objects, like buildings or trees, are visible. However, occlusion or illumination differences might challenge image processing. From an image orientation point of view those multi-camera systems bring the advantage of a better ray intersection geometry compared to nadir-only image blocks. On the other hand, varying scale, occlusion and atmospheric influences which are difficult to model impose problems to the image matching and bundle adjustment tasks. In order to understand current limitations of image orientation approaches and the influence of different parameters such as image overlap or GCP distribution, a commonly available dataset was released. The originally captured data comprises of a state-of-the-art image block with very high overlap, but in the first stage of the so-called ISPRS/EUROSDR benchmark on multi-platform photogrammetry only a reduced set of images was released. In this paper some first results obtained with this dataset are presented. They refer to different aspects like tie point matching across the viewing directions, influence of the oblique images onto the bundle adjustment, the role of image overlap and GCP distribution. As far as the tie point matching is concerned we observed that matching of overlapping images pointing to the same cardinal direction, or between nadir and oblique views in general is quite successful. Due to the quite different perspective between images of different viewing directions the standard tie point matching, for instance based on interest points does not work well. How to address occlusion and ambiguities due to different views onto objects is clearly a non-solved research problem so far. In our experiments we also confirm that the obtainable height accuracy is better when all images are used in bundle block adjustment. This was also shown in other research before and is confirmed here. Not surprisingly, the large overlap of 80/80% provides much better object space accuracy – random errors seem to be about 2-3fold smaller compared to the 60/60% overlap. A comparison of different software approaches shows that newly emerged commercial packages, initially intended to work with small frame image blocks, do perform very well.

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42

Li, Xiaolin, Wenhui Dong, Faliang Chang, and Peishu Qu. "Topology Learning of Non-overlapping Multi-camera Network." International Journal of Signal Processing, Image Processing and Pattern Recognition 8, no. 11 (November 30, 2015): 243–54. http://dx.doi.org/10.14257/ijsip.2015.8.11.22.

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Xiang, Zong Jie, Qiren Chen, and Yuncai Liu. "Feature correspondence in a non-overlapping camera network." Multimedia Tools and Applications 73, no. 3 (July 25, 2013): 1129–45. http://dx.doi.org/10.1007/s11042-013-1600-z.

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Zhao, Fangda, Toru Tamaki, Takio Kurita, Bisser Raytchev, and Kazufumi Kaneda. "Marker-based non-overlapping camera calibration methods with additional support camera views." Image and Vision Computing 70 (February 2018): 46–54. http://dx.doi.org/10.1016/j.imavis.2017.12.006.

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Yang, Tianlong, Qiancheng Zhao, Xian Wang, and Dongzhao Huang. "Accurate calibration approach for non-overlapping multi-camera system." Optics & Laser Technology 110 (February 2019): 78–86. http://dx.doi.org/10.1016/j.optlastec.2018.07.054.

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Fawad, Muhammad Jamil Khan, and MuhibUr Rahman. "Person Re-Identification by Discriminative Local Features of Overlapping Stripes." Symmetry 12, no. 4 (April 17, 2020): 647. http://dx.doi.org/10.3390/sym12040647.

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The human visual system can recognize a person based on his physical appearance, even if extreme spatio-temporal variations exist. However, the surveillance system deployed so far fails to re-identify the individual when it travels through the non-overlapping camera’s field-of-view. Person re-identification (Re-ID) is the task of associating individuals across disjoint camera views. In this paper, we propose a robust feature extraction model named Discriminative Local Features of Overlapping Stripes (DLFOS) that can associate corresponding actual individuals in the disjoint visual surveillance system. The proposed DLFOS model accumulates the discriminative features from the local patch of each overlapping strip of the pedestrian appearance. The concatenation of histogram of oriented gradients, Gaussian of color, and the magnitude operator of CJLBP bring robustness in the final feature vector. The experimental results show that our proposed feature extraction model achieves rank@1 matching rate of 47.18% on VIPeR, 64.4% on CAVIAR4REID, and 62.68% on Market1501, outperforming the recently reported models from the literature and validating the advantage of the proposed model.

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Wang, Youlu, Senem Velipasalar, and Mustafa Cenk Gursoy. "Distributed wide-area multi-object tracking with non-overlapping camera views." Multimedia Tools and Applications 73, no. 1 (November 13, 2012): 7–39. http://dx.doi.org/10.1007/s11042-012-1267-x.

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Tian Miao, 田苗, 关棒磊 Guan Banglei, 孙放 Sun Fang, 苑云 Yuan Yun, and 于起峰 Yu Qifeng. "Decoupling Relative Pose Estimation Method for Non-Overlapping Multi-Camera System." Acta Optica Sinica 41, no. 5 (2021): 0515001. http://dx.doi.org/10.3788/aos202141.0515001.

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49

Kemper, G., A. Weidauer, and T. Coppack. "MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 689–94. http://dx.doi.org/10.5194/isprsarchives-xli-b8-689-2016.

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The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

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50

Kemper, G., A. Weidauer, and T. Coppack. "MONITORING SEABIRDS AND MARINE MAMMALS BY GEOREFERENCED AERIAL PHOTOGRAPHY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 689–94. http://dx.doi.org/10.5194/isprs-archives-xli-b8-689-2016.

Full text

Abstract:

The assessment of anthropogenic impacts on the marine environment is challenged by the accessibility, accuracy and validity of biogeographical information. Offshore wind farm projects require large-scale ecological surveys before, during and after construction, in order to assess potential effects on the distribution and abundance of protected species. The robustness of site-specific population estimates depends largely on the extent and design of spatial coverage and the accuracy of the applied census technique. Standard environmental assessment studies in Germany have so far included aerial visual surveys to evaluate potential impacts of offshore wind farms on seabirds and marine mammals. However, low flight altitudes, necessary for the visual classification of species, disturb sensitive bird species and also hold significant safety risks for the observers. Thus, aerial surveys based on high-resolution digital imagery, which can be carried out at higher (safer) flight altitudes (beyond the rotor-swept zone of the wind turbines) have become a mandatory requirement, technically solving the problem of distant-related observation bias. A purpose-assembled imagery system including medium-format cameras in conjunction with a dedicated geo-positioning platform delivers series of orthogonal digital images that meet the current technical requirements of authorities for surveying marine wildlife at a comparatively low cost. At a flight altitude of 425 m, a focal length of 110 mm, implemented forward motion compensation (FMC) and exposure times ranging between 1/1600 and 1/1000 s, the twin-camera system generates high quality 16 bit RGB images with a ground sampling distance (GSD) of 2 cm and an image footprint of 155 x 410 m. The image files are readily transferrable to a GIS environment for further editing, taking overlapping image areas and areas affected by glare into account. The imagery can be routinely screened by the human eye guided by purpose-programmed software to distinguish biological from non-biological signals. Each detected seabird or marine mammal signal is identified to species level or assigned to a species group and automatically saved into a geo-database for subsequent quality assurance, geo-statistical analyses and data export to third-party users. The relative size of a detected object can be accurately measured which provides key information for species-identification. During the development and testing of this system until 2015, more than 40 surveys have produced around 500.000 digital aerial images, of which some were taken in specially protected areas (SPA) of the Baltic Sea and thus include a wide range of relevant species. Here, we present the technical principles of this comparatively new survey approach and discuss the key methodological challenges related to optimizing survey design and workflow in view of the pending regulatory requirements for effective environmental impact assessments.

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