Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Real-time Computer Vision System for Measuring Traffic Parameters
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Multiple Target Tracking by Appearance-Based Condensation Tracker using Structure Information
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Robust Fragments-based Tracking using the Integral Histogram
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
International Journal of Computer Vision
Tracking multiple humans in crowded environment
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Active contours for tracking distributions
IEEE Transactions on Image Processing
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We propose an efficient approach for tracking humans in presence of severe occlusions through a combination of edge and color features. We implement a part based tracking paradigm to localize, accurately, the head, torso and the legs of a human target in successive frames. The Non-parametric color probability density estimates of these parts of the target are used to track them independently using mean shift. A robust edge matching algorithm, then, validates and refines the mean shift estimate of each part. The part based implementation of mean shift along with the novel edge matching algorithm ensures a reliable tracking of humans in upright pose through severe scene as well as inter-object occlusions. We use the CAVIAR Data Set as well as our own IIT Kanpur test cases demonstrating varying levels of occlusion in daily life situations to evaluate our tracking method.