While various online multi-target tracking methods have been proposed recently, most of their runtime speed struggle at 1-10 frames per second for moderate crowded scenes. In this thesis, we present a novel real-time multi-target tracking system based on the tracking-by-detection framework. Our system is designed for tracking a variable number of interacting targets from a single, static, above shoulder camera, which is a general setting for video surveillance. One challenge in our approach is that when background subtraction is used for detecting moving targets, merged measurements occur frequently because of target interactions. To cope with the problem, we propose to use correlation filter based object detector to robustly separate the targets in merged measurements. Then, online object tracking assisted data association is used to solve the track-measurement assignment. To reduce computation load, our object tracking algorithm is assisted by correlations filter based trackers which share the same features used by our object detector. In addition, to recover partially occluded targets, we allow unconfident detections to be assigned to tracks whilst care is taken to avoid introducing additional false positives. We also analyze the online approximation to multi-channel correlation filters. Our experiments show that exact solution is more resistant to noisy channels than approximate solution. Evaluation on generally accepted datasets reveals that the proposed system is comparable to state-of-the-art methods in terms of performance while running several magnitudes faster. Additionally, we show that the proposed system can be readily implemented on the Texas Instruments TMS320C6678 DSP (C6678) without significant degradation in speed or performance. Details on efficient implementation of the system is also discussed. Especially, for computing Histogram of Oriented Gradients (HOG) feature, our optimized implementation runs at 60fps on VGA images on a single core of C6678, which is 10 times faster than a directly ported implementation. / published_or_final_version / Computer Science / Master / Master of Philosophy

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Visual surveillance in dynamic scenes, especially for human activities, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism and crime to ensure public safety. The motivation of this thesis is to design an efficient human visual tracking system for video surveillance deployed in complex environments. In video surveillance, detection of moving objects is the first step to analyze the video streams. And motion segmentation is one of popular approaches to do it. In this thesis, we propose a motion segmentation method to overcome the problem of motion blurring. The task of human tracking is key to the effective use of more advanced technologies, like activity recognition and behavior understanding. However, human tracking routines often fail either due to human's arbitrary movements or occlusions by other objects. To overcome human's arbitrary movement, we propose a new Silhouette Chain Shift model for human detection and tracking. To track human under occlusions, firstly each frame is represented by a scene energy which consists of all the moving objects. Then the process of tracking is converted to a process of minimizing the proposed scene energy. Findings from the thesis contribute to improve the performance of human visual tracking system and therefore improve security in areas under surveillance. / published_or_final_version / Computer Science / Doctoral / Doctor of Philosophy

Not available / text

Thesis (PhD) -- University of South Australia, 1999

Project report (M. Eng.)--Virginia Polytechnic Institute and State University, 1990. / Vita. Abstract. Includes bibliographical references (leaf 71). Also available via the Internet.

Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 130-136). Also available in electronic version.

Thesis (M.S. in mechanical engineering)--Washington State University, May 2007. / Includes bibliographical references (p. 88-89).

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (p. 198-207).