# Multi-target tracking for video surveillance using deep affinity network: a brief review

arXiv: https://arxiv.org/abs/2110.15674 · HTML: https://mlsanity.com/paper/2110.15674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 67 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 18 of 18)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [Fast and Furious: Real Time End-to-End 3D Detection, Tracking and Motion Forecasting with a Single Convolutional Net](https://mlsanity.com/paper/2012.12395/) (2020)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking](https://mlsanity.com/paper/1504.01942/) (2015)
- [Deep Learning in Video Multi-Object Tracking: A Survey](https://mlsanity.com/paper/1907.12740/) (2019)
- [Learning by tracking: Siamese CNN for robust target association](https://mlsanity.com/paper/1604.07866/) (2016)
- [Spatial-Temporal Relation Networks for Multi-Object Tracking](https://mlsanity.com/paper/1904.11489/) (2019)
- [Online Multi-Target Tracking Using Recurrent Neural Networks](https://mlsanity.com/paper/1604.03635/) (2016)
- [Online Multi-Object Tracking Using CNN-based Single Object Tracker with Spatial-Temporal Attention Mechanism](https://mlsanity.com/paper/1708.02843/) (2017)
- [A survey of advances in vision-based vehicle re-identification](https://mlsanity.com/paper/1905.13258/) (2019)
- [Segment as Points for Efficient Online Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2007.01550/) (2020)
- [CVPR19 Tracking and Detection Challenge: How crowded can it get?](https://mlsanity.com/paper/1906.04567/) (2019)
- [A Survey of Appearance Models in Visual Object Tracking](https://mlsanity.com/paper/1303.4803/) (2013)
- [Online Multi-Object Tracking with Instance-Aware Tracker and Dynamic Model Refreshment](https://mlsanity.com/paper/1902.08231/) (2019)
- [Trajectory Factory: Tracklet Cleaving and Re-connection by Deep Siamese Bi-GRU for Multiple Object Tracking](https://mlsanity.com/paper/1804.04555/) (2018)
- [Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking](https://mlsanity.com/paper/1811.10984/) (2018)

## Abstract (arXiv)

Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researcher's attention due to its potential applications in various domains. Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects. In addition to that, the Multitarget tracking framework requires multiple tasks to perform i.e. target detection, estimating trajectory, associations between frame, and re-identification. Various methods have been suggested, and some assumptions are made to constrain the problem in the context of a particular problem. In this paper, the state-of-the-art MTT models, which leverage from deep learning representational power are reviewed.