# Simple online and real-time tracking with occlusion handling

arXiv: https://arxiv.org/abs/2103.04147 · HTML: https://mlsanity.com/paper/2103.04147/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Simple Online and Realtime Tracking with a Deep Association Metric](https://mlsanity.com/paper/1703.07402/) (2017)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [GNN3DMOT: Graph Neural Network for 3D Multi-Object Tracking with Multi-Feature Learning](https://mlsanity.com/paper/2006.07327/) (2020)
- [POI: Multiple Object Tracking with High Performance Detection and Appearance Feature](https://mlsanity.com/paper/1610.06136/) (2016)
- [Online Multi-Target Tracking Using Recurrent Neural Networks](https://mlsanity.com/paper/1604.03635/) (2016)
- [Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor](https://mlsanity.com/paper/1504.02340/) (2015)
- [GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization](https://mlsanity.com/paper/2010.00067/) (2020)
- [A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects](https://mlsanity.com/paper/1607.06317/) (2016)
- [Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking](https://mlsanity.com/paper/1609.09156/) (2016)

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

- [GaitFormer: Learning Gait Representations with Noisy Multi-Task Learning](https://mlsanity.com/paper/2310.19418/) (2023)
- [Fast Online and Relational Tracking](https://mlsanity.com/paper/2208.03659/) (2022)
- [On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition](https://mlsanity.com/paper/2504.07598/) (2025)
- [Long-term Human Participation Assessment In Collaborative Learning Environments Using Dynamic Scene Analysis](https://mlsanity.com/paper/2405.02317/) (2024)

## Abstract (arXiv)

Multiple object tracking is a challenging problem in computer vision due to difficulty in dealing with motion prediction, occlusion handling, and object re-identification. Many recent algorithms use motion and appearance cues to overcome these challenges. But using appearance cues increases the computation cost notably and therefore the speed of the algorithm decreases significantly which makes them inappropriate for online applications. In contrast, there are algorithms that only use motion cues to increase speed, especially for online applications. But these algorithms cannot handle occlusions and re-identify lost objects. In this paper, a novel online multiple object tracking algorithm is presented that only uses geometric cues of objects to tackle the occlusion and reidentification challenges simultaneously. As a result, it decreases the identity switch and fragmentation metrics. Experimental results show that the proposed algorithm could decrease identity switch by 40% and fragmentation by 28% compared to the state of the art online tracking algorithms. The code is also publicly available.