Bibliografías temáticas / Rule-Based Moving Object Tracking

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Literatura académica sobre el tema "Rule-Based Moving Object Tracking"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 9 de febrero de 2022

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Artículos de revistas sobre el tema "Rule-Based Moving Object Tracking"

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Hashimoto, Masafumi, Yosuke Matsui y Kazuhiko Takahashi. "Moving-Object Tracking with In-Vehicle Multi-Laser Range Sensors". Journal of Robotics and Mechatronics 20, n.º 3 (20 de junio de 2008): 367–77. http://dx.doi.org/10.20965/jrm.2008.p0367.

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This paper presents a method for moving-object tracking with in-vehicle 2D laser range sensor (LRS) in a cluttered environment. A sensing area of one LRS is limited in orientation, and hence the mobile robot is equipped with multi-LRSs for omnidirectional sensing. Since each LRS takes the laser image on its own local coordinate frame, the laser image is mapped onto a reference coordinate frame so that the object tracking can be achieved by cooperation of multi-LRSs. For mapping the coordinate frames of multi-LRSs are calibrated, that is, the relative positions and orientations of the multi-LRSs are estimated. The calibration is based on Kalman filter and chi-hypothesis testing. Moving-object tracking is achieved by two steps: detection and tracking. Each LRS finds moving objects from its own laser image via a heuristic rule and an occupancy grid based method. It tracks the moving objects via Kalman filter and the assignment algorithm based data association. When the moving objects exist in the overlapped sensing areas of the LRSs, these LRSs exchange the tracking data and fuse them in a decentralized manner. A rule based track management is embedded into the tracking system in order to enhance the tracking performance. The experimental result of three walking-people tracking in an indoor environment validates the proposed method.
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Yuan, Bao Hong, De Xiang Zhang, Kui Fu y Ling Jun Zhang. "Video Tracking of Human with Occlusion Based on MeanShift and Kalman Filter". Applied Mechanics and Materials 380-384 (agosto de 2013): 3672–77. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3672.

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In order to accomplish tracking of moving objects requirements, and overcome the defect of occlusion in the process of tracking moving object, this paper presents a method which uses a combination of MeanShift and Kalman filter algorithm. MeanShift object tracking algorithm uses a histogram to describe the color characteristics of an object, and search the location of an image region that the color histogram is closest to the histogram of the object. Histogram similarity is defined in terms of the Bhattacharya coefficient. When the moving object is a large area blocked, the future state of moving object is estimated by Kalman filter. Experimental results verify that the proposed algorithm achieves efficient tracking of moving objects under the confusing situations.
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3

Jang, Dae-Sik, Gye-Young Kim y Hyung-Il Choi. "Model-based tracking of moving object". Pattern Recognition 30, n.º 6 (junio de 1997): 999–1008. http://dx.doi.org/10.1016/s0031-3203(96)00128-8.

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Choe, Gwangmin, Tianjiang Wang, Fang Liu, Gwangho Li, Hyongwang O y Songryong Kim. "Moving object tracking based on geogram". Multimedia Tools and Applications 74, n.º 21 (26 de junio de 2014): 9771–94. http://dx.doi.org/10.1007/s11042-014-2150-8.

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Fan, Jian De y Jiang Bo Zhu. "Object Tracking Based on Dual-View Stereo System". Advanced Materials Research 850-851 (diciembre de 2013): 780–83. http://dx.doi.org/10.4028/www.scientific.net/amr.850-851.780.

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Tracking moving objects in dual-view stereo system is becoming a hot research area in computer vision. To capture the moving objects pixels more accurately, we proposed a new object tracking algorithm which first compute moving objects feature points and then match these points, finally connect the matching feature points and get objects motion trajectories. The algorithm was tested in the video sequences with resolution 640×480 and 768×576 individually. The results show that the algorithm is more robust and the trajectories of the moving objects tracked with our method are more accurate compared with current method of L-K optical flow.
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Zhang, Ming Jie y Bao Sheng Kang. "An Improved Moving Object Tracking Method Based on Graph Cuts". Applied Mechanics and Materials 596 (julio de 2014): 398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.596.398.

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In order to improve efficiency of object tracking in occlusion states. A method to detect and automatically track was present in a surveillance system. Firstly, a graph cuts method was employed to segment image from a static scene. To identify foreground objects by positions and sizes of the obtained foreground regions. In addition, the performance to track objects was improved by using the improved overlap tracking method, the tracking method was used to analyze the centroid distance between neighboring objects and help object tracking in occlusion states of merging and splitting. By the experiments of moving object tracking in three video sequences, the experimental results exhibit that the proposed method is better than the traditional method.
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7

Fan, Jianping, Essam A. El-Kwae, Mohand-Said Hacid y Feng Liang. "Novel tracking-based moving object extraction algorithm". Journal of Electronic Imaging 11, n.º 3 (2002): 393. http://dx.doi.org/10.1117/1.1482095.

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Wang, Qun Long. "Human Motion Tracking Based on B-Spline Snake Algorithm". Advanced Materials Research 706-708 (junio de 2013): 1886–89. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1886.

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A new method for tracking of moving human objects is presented. The developed algorithm is based on B-spline Snake model. The Snake algorithm accesses the object contour through minimizing the energy; the object contour in each frame is presented through three B-spline curve. This approach gets the change image with double thresholds image segmentation between the neighboring frames, it could detect moving human objects with high quality and locate objects approximately. The contour extracts from the last frame put at the approximate position as the B-spline Snake model initialization. It accomplishes the tracking of moving human objects.
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9

Gao, Tao. "Data Association Based Tracking Traffic Objects". International Journal of Advanced Pervasive and Ubiquitous Computing 5, n.º 2 (abril de 2013): 31–46. http://dx.doi.org/10.4018/japuc.2013040104.

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For the widely demanding of adaptive multiple moving objects tracking in intelligent transportation field, a new type of traffic video based multi-object tracking method is presented. Background is modeled by difference of Gaussians (DOG) probability kernel and background subtraction is used to detect multiple moving objects. After obtaining the foreground, shadow is eliminated by an edge detection method. A type of particle filtering combined with SIFT method is used for motion tracking. A queue chain method is used to record data association among different objects, which could improve the detection accuracy and reduce the complexity. By actual road tests, the system tracks multi-object with a better performance of real time and mutual occlusion robustness, indicating that it is effective for intelligent transportation system.
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Wang, Zhen Hai y Ki Cheon Hong. "An New Method for Multi-Object Tracking Using Energy Minimization-Based Data Association". Applied Mechanics and Materials 427-429 (septiembre de 2013): 1822–25. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.1822.

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multiple object tracking is an active and important research topic. It faces many challenging problems. Object extraction and data association are two most key steps in multiple object tracking. To improve tracking performance, this paper proposed a tracking method which combines Kalman filter and energy minimization-based data association. Moving objects are segmented through frame difference. Its can be consider as the vertex. All detections in adjacent frames are be used to construct a graph. The energy is finally minimized with a graph cuts optimization. Data association can be consider as multiple labeling problems. Object corresponding can be obtained through energy minimization. Experiment results demonstrate this method can be accurately tracking two moving objects.
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Tesis sobre el tema "Rule-Based Moving Object Tracking"

1

Lin, Chung-Ching. "Detecting and tracking moving objects from a moving platform". Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/49014.

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Detecting and tracking moving objects are important topics in computer vision research. Classical methods perform well in applications of steady cameras. However, these techniques are not suitable for the applications of moving cameras because the unconstrained nature of realistic environments and sudden camera movement makes cues to object positions rather fickle. A major difficulty is that every pixel moves and new background keeps showing up when a handheld or car-mounted camera moves. In this dissertation, a novel estimation method of camera motion parameters will be discussed first. Based on the estimated camera motion parameters, two detection algorithms are developed using Bayes' rule and belief propagation. Next, an MCMC-based feature-guided particle filtering method is presented to track detected moving objects. In addition, two detection algorithms without using camera motion parameters will be further discussed. These two approaches require no pre-defined class or model to be trained in advance. The experiment results will demonstrate robust detecting and tracking performance in object sizes and positions.
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2

Yilmaz, Mehmet. "Multiple Target Tracking Using Multiple Cameras". Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609477/index.pdf.

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Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, crowded public places and borders. The rise in computer speed, availability of cheap large-capacity storage devices and high speed network infrastructure enabled the way for cheaper, multi sensor video surveillance systems. In this thesis, the problem of tracking multiple targets with multiple cameras has been discussed. Cameras have been located so that they have overlapping fields of vision. A dynamic background-modeling algorithm is described for segmenting moving objects from the background, which is capable of adapting to dynamic scene changes and periodic motion, such as illumination change and swaying of trees. After segmentation of foreground scene, the objects to be tracked have been acquired by morphological operations and connected component analysis. For the purpose of tracking the moving objects, an active contour model (snakes) is one of the approaches, in addition to a Kalman tracker. As the main tracking algorithm, a rule based tracker has been developed first for a single camera, and then extended to multiple cameras. Results of used and proposed methods are given in detail.
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Emeksiz, Deniz. "Object Tracking System With Seamless Object Handover Between Stationary And Moving Camera Modes". Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615190/index.pdf.

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As the number of surveillance cameras and mobile platforms with cameras increases, automated detection and tracking of objects on these systems gain importance. There are various tracking methods designed for stationary or moving cameras. For stationary cameras, correspondence based tracking methods along with background subtraction have various advantages such as enabling detection of object entry and exit in a scene. They also provide robust tracking when the camera is static. However, they fail when the camera is moving. Conversely, histogram based methods such as mean shift enables object tracking on moving camera cases. Though, with mean shift object&rsquo
s entry and exit cannot be detected automatically which means a new object&rsquo
s manual initialization is required. In this thesis, we propose a dual-mode object tracking system which combines the benefits of correspondence based tracking and mean shift tracking. For each frame, a reliability measure based on background update rate is calculated. Interquartile Range is used for finding outliers on this measure and camera movement is detected. If the camera is stationary, correspondence based tracking is used and when camera is moving, the system switches to the mean shift tracking mode until the reliability of correspondence based tracking is sufficient according to the reliability measure. The results demonstrate that, in stationary camera mode, new objects can be detected automatically by correspondence based tracking along with background subtraction. When the camera starts to move, generation of false objects by correspondence based tracking is prevented by switching to mean shift tracking mode and handing over the correct bounding boxes with a seamless operation which enables continuous tracking.
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4

Poon, Ho-shan y 潘浩山. "Visual tracking of multiple moving objects in images based on robust estimation of the fundamental matrix". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4322426X.

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Poon, Ho-shan. "Visual tracking of multiple moving objects in images based on robust estimation of the fundamental matrix". Click to view the E-thesis via HKUTO, 2009. http://sunzi.lib.hku.hk/hkuto/record/B4322426X.

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Ting, Shih-hsiang y 丁士翔. "Moving Object Tracking Based on Spatiotemporal Domain Method". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/h592cs.

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碩士
國立中山大學
機械與機電工程學系研究所
96
As a result of everlasting developments in multimedia technologies, all kinds of objects tracking theory using machine vision or image process methods have been proposed. Most of the methods are based on shape of the object. For this reason, the profile of the tracked object must be known in advance. In many situations, we expect to track the object whose shape is unknown but speed or direction is explicit. For instance, speed or moving direction of the object is known. This thesis presents a spatio-temporal tracking technique, which extracts image information depending on speed of the moving object regardless of its shape. Furthermore, combination of the proposed method in spatio-temporal domain and the optical flow scheme makes the whole tracking system even more robust.
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7

Huang, Cheng-Huom y 黃正和. "Group-Based Object Tracking Sensor Networks: Exploiting Group Moving Patterns". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/51868764888753366744.

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碩士
國立交通大學
資訊科學與工程研究所
95
Predication-based techniques are able to reduce the energy consumption in object tracking sensor networks. Prior works exploit mining object moving patterns for prediction-based object tracking sensor network and developed a hierarchical architecture to efficiently track objects. Note that sensors are inherently storage-constrained. Clearly, mining and storing individual object moving patterns unavoidably need a considerable amount of storage spaces in sensor nodes, which is not of practical. Thus, in this paper, we propose a group-based object tracking sensor network (abbreviated as GBOT) which explores the feature of group mobility of objects for storage-constrained object tracking sensor networks. Specifically, we first formulate a dissimilarity function among object moving patterns, where object moving patterns are viewed as emission trees. In light of the dissimilarity function, the dissimilarity relationships among objects are derived. Given dissimilarity relationships among objects, we further propose two clustering schemes to discover group mobility patterns of objects. Furthermore, for each group, we judiciously select one representative emission tree and utilize this emission tree for prediction. In addition, a maintenance algorithm is derived to preserve the prediction accuracy when moving behaviors of objects vary. Experimental results show that GBOT not only effectively reduces storage cost but also has a good prediction accuracy in storage-constrained sensor networks.
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8

Chu, Yuan-Chang y 朱元昌. "Image Moving Object Segmentation and Tracking Based on Multi-Background Model". Thesis, 2008. http://ndltd.ncl.edu.tw/handle/46067691912857745912.

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碩士
雲林科技大學
光學電子工程研究所
96
Intelligent surveillance system is a very popular research subject. In the majority of research, the researchers merely segment simple static background. However in the scenario, there are some changes in background both indoor and outdoor, such as change of illumination, variation of shadow, waving tree, undulate of water and flutter of flag, etc. In this thesis, I propose a method of building background model which can complete the adaptive update determined by the environmental changes. This system is able to fast build static background and update real time. Besides, it could build dynamic background with low space memory. Therefore, it could detect moving object correctly in chaotic background. We take Histogram based matching technique to promote tracking performance and accuracy in the object tracking part. Finally, we use TI Davinci development-platform as the proof. It is able to reach the rate of 7 ~ 8 frames per second by using simple ARM.
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9

Chi, Chun-Jung y 紀俊榮. "Moving Object Detection, Recognition and Tracking Based on Entropy Background Model". Thesis, 2009. http://ndltd.ncl.edu.tw/handle/04825648074752423376.

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碩士
國立彰化師範大學
電子工程學系
97
This thesis proposed an object detection and tracking framework based on an entropy-based background modeling technique. Our research group previously proposed a video object detection method, called ASLEI (Adaptive State-Labeling Entropy Image), which has the advantages of high detection quality, robustness, and low computational complexity. However, as other object detection methods based on temporal differencing, ASLET fails to detect a still object, which keeps still in the video for several consecutive frames. This thesis proposed a new background modeling technique derived from ASLEI. The proposed entropy-based background modeling can achieve a better background registration rate than other background modeling techniques using pixel-based temporal differencing. Also, the still objects in the video can be easily detected by employing the proposed background model. We use the ASLEI algorithm to find the moving object mask (MOM) and use the background model to find the static object mask (SOM). Both MOM and SOM are combined to extract the object blobs from the video sequences. The extracted object blobs are classified as either moving blobs or static blobs according the percentage of blob pixels which belongs to MOM. Then, those static blobs are further classified as static foreground blobs or static background blobs by measuring the foreground similarity for every static blob. Furthermore, we perform a feature analysis to classify the static background blobs as either removed objects or ghosts, and classify the static foreground blobs as either abandoned objects or halted humans. In order to achieve reliable object tracking, we proposed the intersection matching of minimum displacement and the maximum overlapping test, which can help to construct a more reliable match table for object tracking. The experimental results have shown that the proposed framework can perform object detection, recognition, and tracking correctly even in complex scenes. Thus, the proposed object detection and tracking framework is very suitable for building intelligent video surveillance systems.
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10

Huang, Kai-shuo y 黃塏碩. "Object Tracking under a Moving Camera–An Adaptive Color-Texture-based Particle Filter Tracking Algorithm". Thesis, 2010. http://ndltd.ncl.edu.tw/handle/14923449457238614605.

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碩士
國立臺灣科技大學
電機工程系
98
In the last decade, object tracking systems have been widely applied in many different fields due to the rapid development of computer vision techniques and faster computing ability, such as Surveillance System, Health-Care System. In this field, many approaches require establishing background in preprocessing step. This limits tracking algorithm only be executed under a fixed camera. However, many applications are taking place in a moving camera. Accordingly, we propose a new algorithm to track rigid or non-rigid object by a moving camera. The proposed tracking algorithm use rotation-invariant texture feature and color feature to increase the tracking correctness. The target is jointly modeled by color and texture information. We adjust the weight of each feature, so it is less sensitive to different circumstances such as partial occlusions. When fully occluded, we extend search region and double the particle number to avoid missing target if the occlusion disappear. The experimental results reveal that our tracking method can efficiently and successfully track rigid or non-rigid object under appearance and illumination changes. Also, fewer samples are used to achieve better result than the traditional particle filter method.
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Libros sobre el tema "Rule-Based Moving Object Tracking"

1

Boulebtateche, Brahim. Motion tracking of objects in hexagonally-sampled binary images: A development and implementation of a centroid-based algorithm capable of tracking moving objects in a sequence of hexagonally sampled binary images. Bradford, 1987.

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Capítulos de libros sobre el tema "Rule-Based Moving Object Tracking"

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Chen, Jian, Bingxi Jia y Kaixiang Zhang. "Robust Moving Object Tracking Control". En Multi-View Geometry Based Visual Perception and Control of Robotic Systems, 181–200. Boca Raton, FL : CRC Press/Taylor &Francis Group, 2017.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429489211-11.

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Cucchiara, R., C. Grana, G. Neri, M. Piccardi y A. Prati. "The Sakbot System for Moving Object Detection and Tracking". En Video-Based Surveillance Systems, 145–57. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-0913-4_12.

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Mondal, Ajoy, Badri Narayan Subudhi, Moumita Roy, Susmita Ghosh y Ashish Ghosh. "A Study on Nonlinear Classifier-Based Moving Object Tracking". En Advances in Intelligent Systems and Computing, 571–78. New Delhi: Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2012-1_61.

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Kwok, Cody y Dieter Fox. "Map-Based Multiple Model Tracking of a Moving Object". En RoboCup 2004: Robot Soccer World Cup VIII, 18–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-32256-6_2.

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Chen, Wenjie, Yangyang Ma, Zhilei Chai, Mingsong Chen y Daojing He. "An FPGA-Based Real-Time Moving Object Tracking Approach". En Algorithms and Architectures for Parallel Processing, 65–80. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65482-9_5.

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Islam, Md Zahidul, Chi-Min Oh y Chil-Woo Lee. "New Integrated Framework for Video Based Moving Object Tracking". En Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction, 423–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02580-8_46.

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Roberti, Flavio, Juan Marcos Toibero, Jorge A. Sarapura, Víctor Andaluz, Ricardo Carelli y José María Sebastián. "Unified Passivity-Based Visual Control for Moving Object Tracking". En Machine Vision and Navigation, 347–87. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-22587-2_12.

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Hou, Leichao, Junsuo Qu, Ruijun Zhang, Ting Wang y KaiMing Ting. "An Improved Algorithm for Moving Object Tracking Based on EKF". En Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications, 483–90. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03766-6_54.

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Iwahori, Yuji, Toshihiro Takai, Haruki Kawanaka, Hidenori Itoh y Yoshinori Adachi. "Particle Filter Based Tracking of Moving Object from Image Sequence". En Lecture Notes in Computer Science, 401–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11893004_52.

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Gu, Chuang, Touradj Ebrahimi y Murat Kunt. "Morphological Moving Object Segmentation and Tracking for Content-Based Video Coding". En Multimedia Communications and Video Coding, 233–40. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0403-6_29.

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Actas de conferencias sobre el tema "Rule-Based Moving Object Tracking"

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Liu, Kuien, Ke Deng, Zhiming Ding, Xiaofang Zhou y Mingshu Li. "Pattern-based moving object tracking". En the 2011 international workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2030080.2030083.

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Shen, Hao, Shuxiao Li, Jinglan Zhang y Hongxing Chang. "Tracking-based moving object detection". En 2013 20th IEEE International Conference on Image Processing (ICIP). IEEE, 2013. http://dx.doi.org/10.1109/icip.2013.6738637.

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Mitrokhin, Anton, Cornelia Fermuller, Chethan Parameshwara y Yiannis Aloimonos. "Event-Based Moving Object Detection and Tracking". En 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8593805.

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Jie Shao, S. K. Zhou y Qinfen Zheng. "Robust appearance-based tracking of moving object from moving platform". En Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. IEEE, 2004. http://dx.doi.org/10.1109/icpr.2004.1333742.

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Fonseca, Ricardo y Werner Creixell. "Tracking and following a moving object with a quadcopter". En 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017. http://dx.doi.org/10.1109/avss.2017.8078463.

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Chakroun, M., A. Wali y A. M. Alimi. "Multi-agent system for moving object segmentation and tracking". En 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2011. http://dx.doi.org/10.1109/avss.2011.6027366.

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Lin Rui, Du Zhijiang y Sun Lining. "Moving object tracking based on mobile robot vision". En 2009 International Conference on Mechatronics and Automation (ICMA). IEEE, 2009. http://dx.doi.org/10.1109/icma.2009.5246022.

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Hsia, Chih-Hsien, Ding-Wei Huang, Jen-Shiun Chiang y Zong-Jheng Wu. "Moving Object Tracking Using Symmetric Mask-Based Scheme". En 2009 Fifth International Conference on Information Assurance and Security. IEEE, 2009. http://dx.doi.org/10.1109/ias.2009.150.

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Eckenhoff, Kevin, Patrick Geneva, Nathaniel Merrill y Guoquan Huang. "Schmidt-EKF-based Visual-Inertial Moving Object Tracking". En 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. http://dx.doi.org/10.1109/icra40945.2020.9197352.

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Islam, Md Zahidul y Chil-Woo Lee. "Shape based moving object tracking with particle filter". En 2008 International Conference on Control, Automation and Systems (ICCAS). IEEE, 2008. http://dx.doi.org/10.1109/iccas.2008.4694587.

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