Artificial Intelligence - Special volume on computer vision
The Active Recovery of 3D Motion Trajectories and Their Use in Prediction
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume II
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
A Particle Filter without Dynamics for Robust 3D Face Tracking
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Moving Shadow Detection using a Combined Geometric and Color Classification Approach
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
A multiview approach to tracking people in crowded scenes using a planar homography constraint
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Recovery and Reasoning About Occlusions in 3D Using Few Cameras with Applications to 3D Tracking
International Journal of Computer Vision
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We present in this paper a method of tracking multiple objects (people) in 3D for application in video surveillance. The tracking method is designed to work on images with objects at low resolution and has two major contributions. First we propose a way to generate 3D point clouds that imposes multiple constraints (both geometric and appearance-based) to ensure minimal noise in the 3D data. Second, we incorporate a method to group the points into clouds (or clusters) that correspond to objects in the environment being imaged. We show that this method is more powerful than current 3D tracking techniques that try to fuse 2D tracking information into 3D tracks. A comparison to competing 3D tracking methods are shown, and performance and limitations are discussed.