# Online Visual Multi-Object Tracking via Labeled Random Finite Set Filtering

arXiv: https://arxiv.org/abs/1611.06011 · HTML: https://mlsanity.com/paper/1611.06011/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2016.

## Built on by (most cited first, 6 of 6)

- [Visual Multi-Object Tracking with Re-Identification and Occlusion Handling using Labeled Random Finite Sets](https://mlsanity.com/paper/2407.08872/) (2024)
- [Track Initialization and Re-Identification for~3D Multi-View Multi-Object Tracking](https://mlsanity.com/paper/2405.18606/) (2024)
- [Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking](https://mlsanity.com/paper/2312.01650/) (2023)
- [3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving](https://mlsanity.com/paper/2410.14977/) (2024)
- [Survey of Deep Learning Methods for Inverse Problems](https://mlsanity.com/paper/2111.04731/) (2021)
- [A comparison between PMBM Bayesian track initiation and labelled RFS adaptive birth](https://mlsanity.com/paper/2207.06156/) (2022)

## Abstract (arXiv)

This paper proposes an online visual multi-object tracking algorithm using a top-down Bayesian formulation that seamlessly integrates state estimation, track management, clutter rejection, occlusion and mis-detection handling into a single recursion. This is achieved by modeling the multi-object state as labeled random finite set and using the Bayes recursion to propagate the multi-object filtering density forward in time. The proposed filter updates tracks with detections but switches to image data when mis-detection occurs, thereby exploiting the efficiency of detection data and the accuracy of image data. Furthermore the labeled random finite set framework enables the incorporation of prior knowledge that mis-detections of long tracks which occur in the middle of the scene are likely to be due to occlusions. Such prior knowledge can be exploited to improve occlusion handling, especially long occlusions that can lead to premature track termination in on-line multi-object tracking. Tracking performance are compared to state-of-the-art algorithms on well-known benchmark video datasets.