Online multi-target visual tracking using a HISP filter

Baisa, Nathanael L. (2018) Online multi-target visual tracking using a HISP filter. In: 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 27 - 29 January 2018, Funchal, Madeira, Portugal.

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Item Type:Conference or Workshop contribution (Presentation)
Item Status:Live Archive


We propose a new multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like multiple hypothesis tracking (MHT) and point-process-based approaches like probability hypothesis density (PHD) filter, and has a linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. In addition, we alleviate the problem of two or more targets having identical label taking into account the weight propagated with each confirmed hypothesis. Finally, we carry out extensive experiments on Multiple Object Tracking 2016 (MOT16) benchmark dataset and find out that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.

Keywords:Visual Tracking, Multiple Target Filtering, MHT, PHD Filter, HISP Filter, MOT Challenge
Subjects:G Mathematical and Computer Sciences > G740 Computer Vision
Divisions:College of Science > School of Computer Science
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ID Code:31225
Deposited On:22 Mar 2018 12:46

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