With the the ubiquitous deployment of surveillance cameras, huge amounts of video data are being generated at every moment. Analyzing the massive surveillance videos in an efficient manner has become a pressing task. Visual object tracking is one of the enabling technologies for video analysis and has received much attention in the computer vision community during the last decade. Despite the recent advances in the visual tracking research, there are still several challenges to the existing methods such as efficiency, accuracy, resilience to visual ambiguities, etc. To address such challenges and improve the tracking performance, the constraints specific to the surveillance applications need to be utilized, which have not been thoroughly studied before. The objective of this dissertation is to exploit the constraints pertaining to the surveillance applications and integrate them into the probabilistic tracking framework for effective visual tracking.;This dissertation first presents the integration of environment constraints into the particle filtering framework for effectively tracking objects for the urban surveillance applications. In these applications, the movements of objects are constrained by structured environments. Therefore, the relationship between objects and environments can be exploited as additional information for improving the performance of tracking. An environment state is introduced to represent the relationship between the objects and environments. Distance transform is used to model the environment state. The adaptive dynamics model and environment prior are devised for the particle filter to fully utilize the environment information in the tracking process.;Then the integration of electronic localization for effective visual tracking is studied. Electronic signals, like cellular, WiFi and Bluetooth signals emitted from mobile phones, are ubiquitously present and can be associated with the objects of interest. A directional antenna is used for collecting the signals and performing rough electronic localization. Such location information is fed into the visual tracking algorithm as object motion constraints, so the uncertainty and search space of visual tracking are significantly reduced.;Finally, a stereo tracking method for measuring the speed of a moving vehicle within a structured environment is presented. The stereo constraint between the two views and the path constraint for the vehicle's motion are exploited for accurate visual tracking which overcomes the limitation of depth accuracy in long range stereo. In the proposed method, visual stereo tracking and motion estimation in 3D are integrated within the framework of particle filtering. The visual tracking processes in the two views are coupled with each other since they are dependent upon the same 3D motion and correlated in the observations. Considering that the vehicle's motion is physically constrained by the environment, the path constraint reconstructed from stereo views is utilized to reduce the uncertainty about the vehicle's motion and improve the accuracy for both tracking and speed measurement.
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