Title: | Feature-supported Multi-hypothesis Framework for Multi-object Tracking using Kalman Filter |
Authors: | Pathan, Saira Saleem Al-Hamadi, Ayoub Elmezain, Mahmoud Michaelis, Bernd |
Citation: | WSCG '2009: Full Papers Proceedings: The 17th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision in co-operation with EUROGRAPHICS: University of West Bohemia Plzen, Czech Republic, February 2 - 5, 2009, p. 197-202. |
Issue Date: | 2009 |
Publisher: | Václav Skala - UNION Agency |
Document type: | konferenční příspěvek conferenceObject |
URI: | http://wscg.zcu.cz/WSCG2009/Papers_2009/!_WSCG2009_Full_final.zip http://hdl.handle.net/11025/10906 |
ISBN: | 978-80-86943-93-0 |
Keywords: | počítačové vidění;sledování multi-objektů;dopravní dohled;Kalmanův filtr;zpracování obrazu |
Keywords in different language: | computer vision;multi-object tracking;traffic surveillance;Kalman filter;image processing |
Abstract: | A Kalman filter is a recursive estimator and has widely been used for tracking objects. However, unsatisfying tracking of moving objects is observed under complex situations (i.e. inter-object merge and split) which are challenging for classical Kalman filter. This paper describes a multi-hypothesis framework based on multiple features for tracking the moving objects under complex situations using Kalman Tracker. In this framework, a hypothesis (i.e. merge, split, new) is generated on the basis of contextual association probability which identifies the status of the moving objects in the respective occurrences. The association among the moving objects is computed by multi-featured similarity criteria which include spatial size, color and trajectory. Color similarity probability is computed by the correlation-weighted histogram intersection (CWHI). The similarity probabilities of the size and the trajectory are computed and combined with the fused color correlation. The accumulated association probability results in online hypothesis generation. This hypothesis assists Kalman tracker when complex situations appear in real-time tracking (i.e. traffic surveillance, pedestrian tracking). Our algorithm achieves robust tracking with 97.3% accuracy, and 0.07% covariance error in different real-time scenarios. |
Rights: | © Václav Skala - UNION Agency |
Appears in Collections: | WSCG '2009: Full Papers Proceedings |
Files in This Item:
File | Description | Size | Format | |
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Pathan.pdf | Plný text | 729,34 kB | Adobe PDF | View/Open |
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http://hdl.handle.net/11025/10906
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