W4: Real-Time Surveillance of People and Their Activities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
How Does CONDENSATION Behave with a Finite Number of Samples?
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gesture recognition using the Perseus architecture
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Improving tracking by handling occlusions
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Multi-object particle filter tracking with automatic event analysis
Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams
| Hi-index | 0.00 |
Robust and accurate people tracking is a key task in many promising computer-vision applications. One must deal with non-rigid targets in open-world scenarios, whose shape and appearance evolve over time. Targets may interact, causing partial or complete occlusions. This paper improves tracking by means of particle filtering, where occlusions are handled considering the target's predicted trajectories. Model drift is tackled by careful updating, based on the history of likelihood measures. A colour-based likelihood, computed from histogram similarity, is used. Experiments are carried out using sequences from the CAVIAR database.