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We propose a novel online multi-target visual tracker based on the recentlydeveloped Hypothesized and Independent Stochastic Population (HISP) filter. TheHISP filter combines advantages of traditional tracking approaches like MHT andpoint-process-based approaches like PHD filter, and it has linear complexitywhile maintaining track identities. We apply this filter for tracking multipletargets in video sequences acquired under varying environmental conditions andtargets density using a tracking-by-detection approach. We also adopt deep CNNappearance representation by training a verification-identification network(VerIdNet) on large-scale person re-identification data sets. We construct anaugmented likelihood in a principled manner using this deep CNN appearancefeatures and spatio-temporal information. Furthermore, we solve the problem oftwo or more targets having identical label considering the weight propagatedwith each confirmed hypothesis. Extensive experiments on MOT16 and MOT17benchmark data sets show that our tracker significantly outperforms severalstate-of-the-art trackers in terms of tracking accuracy.

Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning