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Thematische Bibliographien / Multi-Camera network / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Multi-Camera network“

Autor: Grafiati

Veröffentlicht am 25. Mai 2024

Zuletzt aktualisiert am 31. Juli 2025

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1

Wu, Yi-Chang, Ching-Han Chen, Yao-Te Chiu, and Pi-Wei Chen. "Cooperative People Tracking by Distributed Cameras Network." Electronics 10, no. 15 (2021): 1780. http://dx.doi.org/10.3390/electronics10151780.

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In the application of video surveillance, reliable people detection and tracking are always challenging tasks. The conventional single-camera surveillance system may encounter difficulties such as narrow-angle of view and dead space. In this paper, we proposed multi-cameras network architecture with an inter-camera hand-off protocol for cooperative people tracking. We use the YOLO model to detect multiple people in the video scene and incorporate the particle swarm optimization algorithm to track the person movement. When a person leaves the area covered by a camera and enters an area covered

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R.Kennady, Et al. "A Nonoverlapping Vision Field Multi-Camera Network for Tracking Human Build Targets." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3 (2023): 366–69. http://dx.doi.org/10.17762/ijritcc.v11i3.9871.

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This research presents a procedure for tracking human build targets in a multi-camera network with nonoverlapping vision fields. The proposed approach consists of three main steps: single-camera target detection, single-camera target tracking, and multi-camera target association and continuous tracking. The multi-camera target association includes target characteristic extraction and the establishment of topological relations. Target characteristics are extracted based on the HSV (Hue, Saturation, and Value) values of each human build movement target, and the space-time topological relations o

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Sharma, Anil, Saket Anand, and Sanjit K. Kaul. "Reinforcement Learning Based Querying in Camera Networks for Efficient Target Tracking." Proceedings of the International Conference on Automated Planning and Scheduling 29 (May 25, 2021): 555–63. http://dx.doi.org/10.1609/icaps.v29i1.3522.

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Surveillance camera networks are a useful monitoring infrastructure that can be used for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on re-identification problems and trajectory association problems. However, as camera networks grow in size, the volume of data generated is humongous, and scalable processing of this data is imperative for deploying practical solutions. In this paper, we address the largely overlooked problem of scheduling cameras for processin

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Zhao, Guoliang, Yuxun Zhou, Zhanbo Xu, Yadong Zhou, and Jiang Wu. "Hierarchical Multi-Supervision Multi-Interaction Graph Attention Network for Multi-Camera Pedestrian Trajectory Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (2022): 4698–706. http://dx.doi.org/10.1609/aaai.v36i4.20395.

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Pedestrian trajectory prediction has become an essential underpinning in various human-centric applications including but not limited to autonomous vehicles, intelligent surveillance system and social robotics. Previous research endeavors mainly focus on single camera trajectory prediction (SCTP), while the problem of multi-camera trajectory prediction (MCTP) is often overly simplified into predicting presence in the next camera. This paper addresses MCTP from a more realistic yet challenging perspective, by redefining the task as a joint estimation of both future destination and possible traj

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Liu, Xin, Herman G. J. Groot, Egor Bondarev, and Peter H. N. de With. "Introducing Scene Understanding to Person Re-Identification using a Spatio-Temporal Multi-Camera Model." Electronic Imaging 2020, no. 10 (2020): 95–1. http://dx.doi.org/10.2352/issn.2470-1173.2020.10.ipas-095.

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In this paper, we investigate person re-identification (re-ID) in a multi-camera network for surveillance applications. To this end, we create a Spatio-Temporal Multi-Camera model (ST-MC model), which exploits statistical data on a person’s entry/exit points in the multi-camera network, to predict in which camera view a person will re-appear. The created ST-MC model is used as a novel extension to the Multiple Granularity Network (MGN) [1], which is the current state of the art in person re-ID. Compared to existing approaches that are solely based on Convolutional Neural Networks (CNNs), our a

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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 (2015): 243–54. http://dx.doi.org/10.14257/ijsip.2015.8.11.22.

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He, Li, Guoliang Liu, Guohui Tian, Jianhua Zhang, and Ze Ji. "Efficient Multi-View Multi-Target Tracking Using a Distributed Camera Network." IEEE Sensors Journal 20, no. 4 (2020): 2056–63. http://dx.doi.org/10.1109/jsen.2019.2949385.

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Li, Yun-Lun, Hao-Ting Li, and Chen-Kuo Chiang. "Multi-Camera Vehicle Tracking Based on Deep Tracklet Similarity Network." Electronics 11, no. 7 (2022): 1008. http://dx.doi.org/10.3390/electronics11071008.

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Multi-camera vehicle tracking at the city scale has received lots of attention in the last few years. It has large-scale differences, frequent occlusion, and appearance differences caused by the viewing angle differences, which is quite challenging. In this research, we propose the Tracklet Similarity Network (TSN) for a multi-target multi-camera (MTMC) vehicle tracking system based on the evaluation of the similarity between vehicle tracklets. In addition, a novel component, Candidates Intersection Ratio (CIR), is proposed to refine the similarity. It provides an associate scheme to build the

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Truong, Philips, Deligiannis, Abrahamyan, and Guan. "Automatic Multi-Camera Extrinsic Parameter Calibration Based on Pedestrian Torsors †." Sensors 19, no. 22 (2019): 4989. http://dx.doi.org/10.3390/s19224989.

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Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedest

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Guan, Banglei, Xiangyi Sun, Yang Shang, Xiaohu Zhang, and Manuel Hofer. "Multi-camera networks for motion parameter estimation of an aircraft." International Journal of Advanced Robotic Systems 14, no. 1 (2017): 172988141769231. http://dx.doi.org/10.1177/1729881417692312.

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A multi-camera network is proposed to estimate an aircraft’s motion parameters relative to the reference platform in large outdoor fields. Multiple cameras are arranged to cover the aircraft’s large-scale motion spaces by field stitching. A camera calibration method using dynamic control points created by a multirotor unmanned aerial vehicle is presented under the conditions that the field of view of the cameras is void. The relative deformation of the camera network caused by external environmental factors is measured and compensated using a combination of cameras and laser rangefinders. A se

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Sumathy, R. "Face Recognition in Multi Camera Network with Sh Feature." International Journal of Modern Education and Computer Science 7, no. 5 (2015): 59–64. http://dx.doi.org/10.5815/ijmecs.2015.05.08.

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Elwarfalli, Hamed, Dylan Flaute, and Russell C. Hardie. "Exponential Fusion of Interpolated Frames Network (EFIF-Net): Advancing Multi-Frame Image Super-Resolution with Convolutional Neural Networks." Sensors 24, no. 1 (2024): 296. http://dx.doi.org/10.3390/s24010296.

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Convolutional neural networks (CNNs) have become instrumental in advancing multi-frame image super-resolution (SR), a technique that merges multiple low-resolution images of the same scene into a high-resolution image. In this paper, a novel deep learning multi-frame SR algorithm is introduced. The proposed CNN model, named Exponential Fusion of Interpolated Frames Network (EFIF-Net), seamlessly integrates fusion and restoration within an end-to-end network. Key features of the new EFIF-Net include a custom exponentially weighted fusion (EWF) layer for image fusion and a modification of the Re

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Raghavendra, Shashank Horakodige, Yashasvi Sorapalli, Nehashri Poojar S. V., et al. "Multi-camera multi-person tracking with DeepSORT and MySQL." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 997. https://doi.org/10.11591/ijeecs.v38.i2.pp997-1009.

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Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object tracki

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Shashank, Horakodige Raghavendra Yashasvi Sorapalli Nehashri Poojar S. V. Hrithik Maddirala Ramakanth Kumar P. Azra Nasreen Neeta Trivedi Ashish Agarwal Sreelakshmi K. "Multi-camera multi-person tracking with DeepSORT and MySQL." Indonesian Journal of Electrical Engineering and Computer Science 38, no. 2 (2025): 997–1009. https://doi.org/10.11591/ijeecs.v38.i2.pp997-1009.

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Multi-camera multi-object tracking refers to the process of simultaneously tracking numerous objects using a network of connected cameras. Constructing an accurate depiction of an object’s movements requires the analysis of video data from many camera feeds, detection of items of interest, and their association across various camera perspectives. The objective is to accurately estimate the trajectories of the objects as they navigate through a monitored area. It has several uses, including surveillance, robotics, self-driving cars, and augmented reality. The current version of an object

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Ye, Weijun, and Bingyang Wang. "Color Restoration Method of Art Images Based on Perceptual Network and Oriented to Serial Data Application in Wireless Systems." International Journal of Swarm Intelligence Research 16, no. 1 (2025): 1–16. https://doi.org/10.4018/ijsir.372083.

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Due to the influence of camera Angle, camera exposure parameters and other reasons, the color and brightness of art painting images are significantly different. Therefore, this paper proposes a color correction method for art painting images based on perceptual network for the application of sequence data in wireless systems. When the damaged area is large or the semantic information is missing, the restoration effect will be greatly reduced. Therefore, a high-resolution generative network is designed as the back-end of the network to improve the resolution of the density map and further impro

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Huang, Sunan, Rodney Swee Huat Teo, and William Wai Lun Leong. "Multi-Camera Networks for Coverage Control of Drones." Drones 6, no. 3 (2022): 67. http://dx.doi.org/10.3390/drones6030067.

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Multiple unmanned multirotor (MUM) systems are becoming a reality. They have a wide range of applications such as for surveillance, search and rescue, monitoring operations in hazardous environments and providing communication coverage services. Currently, an important issue in MUM is coverage control. In this paper, an existing coverage control algorithm has been extended to incorporate a new sensor model, which is downward facing and allows pan-tilt-zoom (PTZ). Two new constraints, namely view angle and collision avoidance, have also been included. Mobile network coverage among the MUMs is s

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Huang, Zhengyue, Zhehui Zhao, Hengguang Zhou, Xibin Zhao, and Yue Gao. "DeepCCFV: Camera Constraint-Free Multi-View Convolutional Neural Network for 3D Object Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8505–12. http://dx.doi.org/10.1609/aaai.v33i01.33018505.

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3D object retrieval has a compelling demand in the field of computer vision with the rapid development of 3D vision technology and increasing applications of 3D objects. 3D objects can be described in different ways such as voxel, point cloud, and multi-view. Among them, multi-view based approaches proposed in recent years show promising results. Most of them require a fixed predefined camera position setting which provides a complete and uniform sampling of views for objects in the training stage. However, this causes heavy over-fitting problems which make the models failed to generalize well

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Николаев, Д. А. "The architecture of software for multi-camera tracking in video surveillance systems with a decentralized structure." МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ 8, no. 4(31) (2020): 10–11. http://dx.doi.org/10.26102/2310-6018/2020.31.4.010.

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В статье представлен вариант архитектурных решений программного обеспечения, поддерживающего специальную функцию видеоаналитики – многокамерное сопровождение в системах видеонаблюдения, реализованных по принципу децентрализованного обмена управляющей информацией. На основе возможностей имеющихся аппаратных платформ интеллектуальных камер видеонаблюдения, а также проведенного анализа и обобщения существующих архитектур распределенных вычислительных систем предложены подходы к функциональному проектированию и последующей реализации модулей программного обеспечения, обеспечивающих протокол обмена

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PENALOZA, Christian, Yasushi MAE, Tatsuo ARAI, Kenichi OHARA, and Tomohito TAKUBO. "2P1-Q04 Multi-Appearance Object Modeling using Camera Network in Household Environment(Intelligent and Robotic Room)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2011 (2011): _2P1—Q04_1—_2P1—Q04_4. http://dx.doi.org/10.1299/jsmermd.2011._2p1-q04_1.

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Cho, Myungjin, Howon Lee, Hyun-Ho Choi, and Bahram Javidi. "A Three-Dimensional Image Transmission Using In-Network Computation in Wireless Multi-Camera Networks." IEEE Journal of the Electron Devices Society 5, no. 6 (2017): 445–52. http://dx.doi.org/10.1109/jeds.2017.2721368.

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Guo, Yuhao, and Hui Hu. "Multi-Layer Fusion 3D Object Detection via Lidar Point Cloud and Camera Image." Applied Sciences 14, no. 4 (2024): 1348. http://dx.doi.org/10.3390/app14041348.

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Object detection is a key task in automatic driving, and the poor performance of small object detection is a challenge that needs to be overcome. Previously, object detection networks could detect large-scale objects in ideal environments, but detecting small objects was very difficult. To address this problem, we propose a multi-layer fusion 3D object detection network. First, a dense fusion (D-fusion) method is proposed, which is different from the traditional fusion method. By fusing the feature maps of each layer, more semantic information of the fusion network can be preserved. Secondly,

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Kim, Seong Hyun, and Ju Yong Chang. "Single-Shot 3D Multi-Person Shape Reconstruction from a Single RGB Image." Entropy 22, no. 8 (2020): 806. http://dx.doi.org/10.3390/e22080806.

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Although the performance of the 3D human shape reconstruction method has improved considerably in recent years, most methods focus on a single person, reconstruct a root-relative 3D shape, and rely on ground-truth information about the absolute depth to convert the reconstruction result to the camera coordinate system. In this paper, we propose an end-to-end learning-based model for single-shot, 3D, multi-person shape reconstruction in the camera coordinate system from a single RGB image. Our network produces output tensors divided into grid cells to reconstruct the 3D shapes of multiple perso

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Hong, Yong, Deren Li, Shupei Luo, Xin Chen, Yi Yang, and Mi Wang. "An Improved End-to-End Multi-Target Tracking Method Based on Transformer Self-Attention." Remote Sensing 14, no. 24 (2022): 6354. http://dx.doi.org/10.3390/rs14246354.

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Current multi-target multi-camera tracking algorithms demand increased requirements for re-identification accuracy and tracking reliability. This study proposed an improved end-to-end multi-target tracking algorithm that adapts to multi-view multi-scale scenes based on the self-attentive mechanism of the transformer’s encoder–decoder structure. A multi-dimensional feature extraction backbone network was combined with a self-built raster semantic map which was stored in the encoder for correlation and generated target position encoding and multi-dimensional feature vectors. The decoder incorpor

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Boyle, Liam, Petra Helmholz, Derek D. Lichti, and Roslyn Ward. "Validation of Camera Networks Used for the Assessment of Speech Movements." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-2-2024 (June 11, 2024): 41–48. http://dx.doi.org/10.5194/isprs-archives-xlviii-2-2024-41-2024.

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Abstract. The term speech sound disorder describes a range of speech difficulties in children that affect speech intelligibility. Differential diagnosis is difficult and reliant on access to validated and reliable measures. Technological advances aim to provide clinical access to measurements that have been identified as beneficial in diagnosing speech disorders. To generate objective measurements and, consequently, automatic scores, the output from multi-camera networks is required to produce quality results. The quality of photogrammetric results is usually expressed in terms of the precisio

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Voulodimos, Athanasios S., Nikolaos D. Doulamis, Dimitrios I. Kosmopoulos, and Theodora A. Varvarigou. "IMPROVING MULTI-CAMERA ACTIVITY RECOGNITION BY EMPLOYING NEURAL NETWORK BASED READJUSTMENT." Applied Artificial Intelligence 26, no. 1-2 (2012): 97–118. http://dx.doi.org/10.1080/08839514.2012.629540.

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Bi Song and A. K. Roy-Chowdhury. "Robust Tracking in A Camera Network: A Multi-Objective Optimization Framework." IEEE Journal of Selected Topics in Signal Processing 2, no. 4 (2008): 582–96. http://dx.doi.org/10.1109/jstsp.2008.925992.

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Rosas-Cervantes, Vinicio, Quoc-Dong Hoang, Sooho Woo, and Soon-Geul Lee. "Mobile robot 3D trajectory estimation on a multilevel surface with multimodal fusion of 2D camera features and a 3D light detection and ranging point cloud." International Journal of Advanced Robotic Systems 19, no. 2 (2022): 172988062210891. http://dx.doi.org/10.1177/17298806221089198.

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Nowadays, multi-sensor fusion is a popular tool for feature recognition and object detection. Integrating various sensors allows us to obtain reliable information about the environment. This article proposes a 3D robot trajectory estimation based on a multimodal fusion of 2D features extracted from color images and 3D features from 3D point clouds. First, a set of images was collected using a monocular camera, and we trained a Faster Region Convolutional Neural Network. Using the Faster Region Convolutional Neural Network, the robot detects 2D features from camera input and 3D features using t

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Zhang, Yi, and J. Chen. "A Intelligent Wheelchair Obstacle Avoidance System Based on Multi-Sensor Fusion Technology." Key Engineering Materials 455 (December 2010): 121–26. http://dx.doi.org/10.4028/www.scientific.net/kem.455.121.

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In this paper an intelligent wheelchair obstacle avoidance system based on multi-sensor data fusion technology is instructed. It giving rises to the hardware architecture of the wheelchair and develops a sonar and camera data acquisition system on the VC++ platform by which we could complete the sonar and camera sensor information collection and data processing. Use a T-S model based fuzzy neural network multi-sensor data fusion method for intelligent wheelchair obstacle avoidance. Some simulations were done to test the method in different environments and the method can effectively integrated

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Vandendriessche, Jurgen, Bruno da Silva, Lancelot Lhoest, An Braeken, and Abdellah Touhafi. "M3-AC: A Multi-Mode Multithread SoC FPGA Based Acoustic Camera." Electronics 10, no. 3 (2021): 317. http://dx.doi.org/10.3390/electronics10030317.

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Acoustic cameras allow the visualization of sound sources using microphone arrays and beamforming techniques. The required computational power increases with the number of microphones in the array, the acoustic images resolution, and in particular, when targeting real-time. Such a constraint limits the use of acoustic cameras in many wireless sensor network applications (surveillance, industrial monitoring, etc.). In this paper, we propose a multi-mode System-on-Chip (SoC) Field-Programmable Gate Arrays (FPGA) architecture capable to satisfy the high computational demand while providing wirele

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Osiński, Piotr, Jakub Markiewicz, Jarosław Nowisz, Michał Remiszewski, Albert Rasiński, and Robert Sitnik. "A Novel Approach for Dynamic (4d) Multi-View Stereo System Camera Network Design." Sensors 22, no. 4 (2022): 1576. http://dx.doi.org/10.3390/s22041576.

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Image network design is a critical factor in image-based 3D shape reconstruction and data processing (especially in the application of combined SfM/MVS methods). This paper aims to present a new approach to designing and planning multi-view imaging networks for dynamic 3D scene reconstruction without preliminary information about object geometry or location. The only constraints are the size of defined measurement volume, the required resolution, and the accuracy of geometric reconstruction. The proposed automatic camera network design method is based on the Monte Carlo algorithm and a set of

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He, Fangzhou. "Exploration of Multi-Node Collaborative Image Acquisition and Compression Techniques for Wireless Multimedia Sensor Networks." International Journal of Online and Biomedical Engineering (iJOE) 15, no. 01 (2019): 196. http://dx.doi.org/10.3991/ijoe.v15i01.9787.

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<span lang="EN-US">Aiming at saving energy and maximizing the network life cycle, the multi-node cooperative image acquisition and compression technology in Wireless Multimedia Sensor Networks</span><span lang="EN-US">(</span><span lang="EN-US">WMSNs) is studied deeply. </span><span lang="EN-US">T</span><span lang="EN-US">he Minimum Energy Image Collection (MEIC) problem for multiple target domains in a certain period of time in the monitoring area is proposed, the integer linear programming for Minimum Energy Image Collection (MEIC) proble

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Gao, Fei, Meizhen Wang, Xuejun Liu, and Ziran Wang. "A multi-objective scheduling optimization algorithm of a camera network for directional road network coverage." PLOS ONE 13, no. 10 (2018): e0206038. http://dx.doi.org/10.1371/journal.pone.0206038.

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Song, Jiaman. "Evaluation Method for Road Area Coverage of Roadside Surveillance Cameras." Highlights in Science, Engineering and Technology 126 (January 10, 2025): 65–71. https://doi.org/10.54097/6pmvxv78.

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As the number of surveillance cameras has increased rapidly, the issues of coverage blindness, coverage overlap and lack of vision resulting from an irrational surveillance layout have become increasingly prominent. This has led to a waste of resources and a reduction in surveillance quality. The conventional methodology for evaluating road coverage is inadequate for accurately assessing the actual monitoring capacity of the camera. In light of the aforementioned issues, this paper puts forth a novel road coverage assessment method based on the monitoring camera perception model. This approach

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Rakhi Madhukararao Joshi. "Enhancing Vehicle Tracking and Recognition Across Multiple Cameras with Multimodal Contrastive Domain Sharing GAN and Topological Embeddings." Panamerican Mathematical Journal 34, no. 1 (2024): 114–27. http://dx.doi.org/10.52783/pmj.v34.i1.910.

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Using Multimodal Contrastive Domain Sharing Generative Adversarial Networks (GAN) and topological embeddings, this study shows a new way to improve car tracking and classification across multiple camera feeds. Different camera angles and lighting conditions can make it hard for current car tracking systems to work correctly. This study tries to solve these problems. Common Objects in Context (COCO) and ImageNet are two datasets that are used in this method for training. Multimodal Contrastive Domain Sharing GAN is used for detection and tracking. It makes cross-modal learning easier by letting

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Heo, Jinyeong, and Yongjin (James) Kwon. "3D Vehicle Trajectory Extraction Using DCNN in an Overlapping Multi-Camera Crossroad Scene." Sensors 21, no. 23 (2021): 7879. http://dx.doi.org/10.3390/s21237879.

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The 3D vehicle trajectory in complex traffic conditions such as crossroads and heavy traffic is practically very useful in autonomous driving. In order to accurately extract the 3D vehicle trajectory from a perspective camera in a crossroad where the vehicle has an angular range of 360 degrees, problems such as the narrow visual angle in single-camera scene, vehicle occlusion under conditions of low camera perspective, and lack of vehicle physical information must be solved. In this paper, we propose a method for estimating the 3D bounding boxes of vehicles and extracting trajectories using a

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Bazi, Yakoub, Haikel Alhichri, Naif Alajlan, and Farid Melgani. "Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks." Applied Sciences 9, no. 23 (2019): 5062. http://dx.doi.org/10.3390/app9235062.

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In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse description of images. The core idea of CSVM is to use a set of linear SVMs as filter banks for feature map generation. During the training phase, the weights of the SVM filters are obtained using a forward-supervised learning strategy unlike the backpropagation algorithm used in standard convolutional neural networks (CNNs). To handle multi-label detec

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Stamatopoulos, C., and C. S. Fraser. "Automated Target-Free Network Orienation and Camera Calibration." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-5 (May 28, 2014): 339–46. http://dx.doi.org/10.5194/isprsannals-ii-5-339-2014.

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Automated close-range photogrammetric network orientation and camera calibration has traditionally been associated with the use of coded targets in the object space to allow for an initial relative orientation (RO) and subsequent spatial resection of the images. However, over the last decade, advances coming mainly from the computer vision (CV) community have allowed for fully automated orientation via feature-based matching techniques. There are a number of advantages in such methodologies for various types of applications, as well as for cases where the use of artificial targets might be not

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Fan, Zhijie, Zhiwei Cao, Xin Li, Chunmei Wang, Bo Jin, and Qianjin Tang. "Video Surveillance Camera Identity Recognition Method Fused With Multi-Dimensional Static and Dynamic Identification Features." International Journal of Information Security and Privacy 17, no. 1 (2023): 1–18. http://dx.doi.org/10.4018/ijisp.319304.

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With the development of smart cities, video surveillance networks have become an important infrastructure for urban governance. However, by replacing or tampering with surveillance cameras, an important front-end device, attackers are able to access the internal network. In order to identify illegal or suspicious camera identities in advance, a camera identity identification method that incorporates multidimensional identification features is proposed. By extracting the static information of cameras and dynamic traffic information, a camera identity system that incorporates explicit, implicit,

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Hong, Jongkwang, Bora Cho, Yong Hong, and Hyeran Byun. "Contextual Action Cues from Camera Sensor for Multi-Stream Action Recognition." Sensors 19, no. 6 (2019): 1382. http://dx.doi.org/10.3390/s19061382.

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In action recognition research, two primary types of information are appearance and motion information that is learned from RGB images through visual sensors. However, depending on the action characteristics, contextual information, such as the existence of specific objects or globally-shared information in the image, becomes vital information to define the action. For example, the existence of the ball is vital information distinguishing “kicking” from “running”. Furthermore, some actions share typical global abstract poses, which can be used as a key to classify actions. Based on these obser

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Li, Hongchao, Chenglong Li, Xianpeng Zhu, Aihua Zheng, and Bin Luo. "Multi-Spectral Vehicle Re-Identification: A Challenge." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11345–53. http://dx.doi.org/10.1609/aaai.v34i07.6796.

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Vehicle re-identification (Re-ID) is a crucial task in smart city and intelligent transportation, aiming to match vehicle images across non-overlapping surveillance camera views. Currently, most works focus on RGB-based vehicle Re-ID, which limits its capability of real-life applications in adverse environments such as dark environments and bad weathers. IR (Infrared) spectrum imaging offers complementary information to relieve the illumination issue in computer vision tasks. Furthermore, vehicle Re-ID suffers a big challenge of the diverse appearance with different views, such as trucks. In t

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Dong, Xuan, Weixin Li, and Xiaojie Wang. "Pyramid convolutional network for colorization in monochrome-color multi-lens camera system." Neurocomputing 450 (August 2021): 129–42. http://dx.doi.org/10.1016/j.neucom.2021.04.014.

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MING, An-Long, Hua-Dong MA, and Hui-Yuan FU. "Bayes Causal Network Based Method for Role Identification in Multi-Camera Surveillance." Chinese Journal of Computers 33, no. 12 (2011): 2378–86. http://dx.doi.org/10.3724/sp.j.1016.2010.02378.

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Canedo-Rodriguez, A., C. V. Regueiro, R. Iglesias, V. Alvarez-Santos, and X. M. Pardo. "Self-organized multi-camera network for ubiquitous robot deployment in unknown environments." Robotics and Autonomous Systems 61, no. 7 (2013): 667–75. http://dx.doi.org/10.1016/j.robot.2012.08.014.

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Yao, Hongwei, Tong Qiao, Ming Xu, and Ning Zheng. "Robust Multi-Classifier for Camera Model Identification Based on Convolution Neural Network." IEEE Access 6 (2018): 24973–82. http://dx.doi.org/10.1109/access.2018.2832066.

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Wang, Sijie, Qiyu Kang, Rui She, Wee Peng Tay, Andreas Hartmannsgruber, and Diego Navarro Navarro. "RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (2023): 6209–16. http://dx.doi.org/10.1609/aaai.v37i5.25765.

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Camera relocalization has various applications in autonomous driving. Previous camera pose regression models consider only ideal scenarios where there is little environmental perturbation. To deal with challenging driving environments that may have changing seasons, weather, illumination, and the presence of unstable objects, we propose RobustLoc, which derives its robustness against perturbations from neural differential equations. Our model uses a convolutional neural network to extract feature maps from multi-view images, a robust neural differential equation diffusion block module to diffu

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Jiang, Mingjun, Zihan Zhang, Kohei Shimasaki, Shaopeng Hu, and Idaku Ishii. "Multi-Thread AI Cameras Using High-Speed Active Vision System." Journal of Robotics and Mechatronics 34, no. 5 (2022): 1053–62. http://dx.doi.org/10.20965/jrm.2022.p1053.

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In this study, we propose a multi-thread artificial intelligence (AI) camera system that can simultaneously recognize remote objects in desired multiple areas of interest (AOIs), which are distributed in a wide field of view (FOV) by using single image sensor. The proposed multi-thread AI camera consists of an ultrafast active vision system and a convolutional neural network (CNN)-based ultrafast object recognition system. The ultrafast active vision system can function as multiple virtual cameras with high spatial resolution by synchronizing exposure of a high-speed camera and movement of an

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Zhou, Jing, Shi Jun Li, Dong Yan Huang, Xiao Lin Chen, and Ye Chi Zhang. "Data Normalization of Single Camera Visual Measurement Network System." Applied Mechanics and Materials 263-266 (December 2012): 2381–84. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2381.

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With the development of visual measurement system , the visual measurement system of single camera which applied in the imaging theory of optical feature points , it has been widespread used in the modern production . Due to the limit of the environment in scene , the visual measurement system of single camera could not measure the shield between the measured objects each other . Focus on this problem , present a kind of the the knowledge of measurement network based on the visual measurement of single camera , set up the measurement network system via the multi-control points . Measure the op

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Alsadik, Bashar, Luuk Spreeuwers, Farzaneh Dadrass Javan, and Nahuel Manterola. "Mathematical Camera Array Optimization for Face 3D Modeling Application." Sensors 23, no. 24 (2023): 9776. http://dx.doi.org/10.3390/s23249776.

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Camera network design is a challenging task for many applications in photogrammetry, biomedical engineering, robotics, and industrial metrology, among other fields. Many driving factors are found in the camera network design including the camera specifications, object of interest, and type of application. One of the interesting applications is 3D face modeling and recognition which involves recognizing an individual based on facial attributes derived from the constructed 3D model. Developers and researchers still face difficulty in reaching the required high level of accuracy and reliability n

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Perfetti, L., C. Polari, and F. Fassi. "FISHEYE MULTI-CAMERA SYSTEM CALIBRATION FOR SURVEYING NARROW AND COMPLEX ARCHITECTURES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 877–83. http://dx.doi.org/10.5194/isprs-archives-xlii-2-877-2018.

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Narrow spaces and passages are not a rare encounter in cultural heritage, the shape and extension of those areas place a serious challenge on any techniques one may choose to survey their 3D geometry. Especially on techniques that make use of stationary instrumentation like terrestrial laser scanning. The ratio between space extension and cross section width of many corridors and staircases can easily lead to distortions/drift of the 3D reconstruction because of the problem of propagation of uncertainty. This paper investigates the use of fisheye photogrammetry to produce the 3D reconstruction

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Dong, Huanan, Ming Wen, and Zhouwang Yang. "Vehicle Speed Estimation Based on 3D ConvNets and Non-Local Blocks." Future Internet 11, no. 6 (2019): 123. http://dx.doi.org/10.3390/fi11060123.

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Vehicle speed estimation is an important problem in traffic surveillance. Many existing approaches to this problem are based on camera calibration. Two shortcomings exist for camera calibration-based methods. First, camera calibration methods are sensitive to the environment, which means the accuracy of the results are compromised in some situations where the environmental condition is not satisfied. Furthermore, camera calibration-based methods rely on vehicle trajectories acquired by a two-stage tracking and detection process. In an effort to overcome these shortcomings, we propose an altern

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