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

arXiv: https://arxiv.org/abs/1908.03945 · HTML: https://mlsanity.com/paper/1908.03945/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2019.

## Built on by (most cited first, 4 of 4)

- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [BASE: Probably a Better Approach to Multi-Object Tracking](https://mlsanity.com/paper/2309.12035/) (2023)
- [STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking](https://mlsanity.com/paper/2201.06824/) (2022)
- [MapTrack: Tracking in the Map](https://mlsanity.com/paper/2402.12968/) (2024)

## Abstract (arXiv)

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.