CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Tracking non-rigid, moving objects based on color cluster flow
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust object tracking with background-weighted local kernels
Computer Vision and Image Understanding
An architecture for a self configurable video supervision
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Tracking by Affine Kernel Transformations Using Color and Boundary Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Independent component analysis-based background subtraction for indoor surveillance
IEEE Transactions on Image Processing
Performance evaluation of object detection algorithms for video surveillance
IEEE Transactions on Multimedia
Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Background Subtraction Using Spatial Cues
IEEE Transactions on Circuits and Systems for Video Technology
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Bayesian filter based behavior recognition in workflows allowing for user feedback
Computer Vision and Image Understanding
Behavior recognition from video based on human constrained descriptor and adaptable neural networks
Proceedings of the 4th ACM/IEEE international workshop on Analysis and retrieval of tracked events and motion in imagery stream
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In this paper, we propose an automatic tracking recovery tool which improves the performance of any tracking algorithm each time the results are not acceptable. For the recovery, we include an object identification task, implemented through an adaptable neural network structure, which classifies image regions as objects. The neural network structure is automatically modified whenever environmental changes occur to improve object classification in very complex visual environments like the examined one. The architecture is enhanced by a decision mechanism which permits verification of the time instances in which track-ing recovery should take place. Experimental results on a set of different video sequences that present complex visual phenomena reveal the efficiency of the proposed scheme in proving tracking in very difficult visual content conditions. abstract environment.