Towards model-based recognition of human movements in image sequences
CVGIP: Image Understanding
Automatic symbolic traffic scene analysis using belief networks
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
W4: Who? When? Where? What? A Real Time System for Detecting and Tracking People
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking of Multiple, Partially Occluded Humans based on Static Body Part Detection
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
"Hybrid Cone-Cylinder" Codebook Model for Foreground Detection with Shadow and Highlight Suppression
AVSS '06 Proceedings of the IEEE International Conference on Video and Signal Based Surveillance
International Journal of Computer Vision
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Object Tracking by Hierarchical Association of Detection Responses
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Real-time foreground-background segmentation using codebook model
Real-Time Imaging
Rapid and robust human detection and tracking based on omega-shape features
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A stochastic graph evolution framework for robust multi-target tracking
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Unconstrained multiple-people tracking
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications
IEEE Transactions on Image Processing
On the use of a minimal path approach for target trajectory analysis
Pattern Recognition
Novel multiclass classification for home-based diagnosis of sleep apnea hypopnea syndrome
Expert Systems with Applications: An International Journal
Multiple human tracking system for unpredictable trajectories
Machine Vision and Applications
Hi-index | 12.05 |
Multiple-target tracking is a challenging field specially when dealing with uncontrolled scenarios. Two common approaches are often used, one based on low-level techniques to detect each object size, position and velocity, and other based on high-level techniques that deal with object appearance. None of these methods can deal with all possible problems in multiple-target tracking: environment occlusions, both total and partial, and collisions, such as grouping and splitting events. So one solution is to merge these techniques to improve their performance. Based on an existing hierarchical architecture, we present a novel technique that can deal with all the mentioned problems in multiple tracking targets. Blob detection, low-level tracking using adaptive filters, high-level tracking based on a fixed pool of histograms and an event management that can detect every collision event and performs occlusion recovery are used to be able to track every object during the time they appear within the scene. Experimental results show the performance of this technique under multiple situations, being able to track every object in the scene without losing their initial identification. The speed processing is higher than 50 frames, which allows it to be used under real-time scenarios.