Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning

Baisa, N.L. (2021) Robust online multi-target visual tracking using a HISP filter with discriminative deep appearance learning. Journal of Visual Communication and Image Representation, 77. ISSN 1047-3203

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Abstract

We propose a novel online 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 MHT and point-process-based approaches like PHD filter, and it has 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. We also adopt deep CNN appearance representation by training a verification-identification network (VerIdNet) on large-scale person re-identification data sets. We construct an augmented likelihood in a principled manner using this deep CNN appearance features and spatio-temporal information. Furthermore, we solve the problem of two or more targets having identical label considering the weight propagated with each confirmed hypothesis. Extensive experiments on MOT16 and MOT17 benchmark data sets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy.

Item Type:
Journal Article
Journal or Publication Title:
Journal of Visual Communication and Image Representation
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2208
Subjects:
ID Code:
165484
Deposited By:
Deposited On:
02 Feb 2022 15:45
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Apr 2022 03:16