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

TL;DR

This paper introduces a robust online multi-target visual tracking method combining the HISP filter with deep CNN appearance features, achieving high accuracy and identity preservation in complex video scenarios.

Contribution

The paper presents a novel multi-target tracking approach that integrates the HISP filter with deep learning-based appearance modeling, enhancing tracking robustness and identity maintenance.

Findings

Outperforms state-of-the-art trackers on MOT16 and MOT17 datasets.

Maintains track identities with linear complexity.

Effectively handles targets with identical labels.

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.