# Online and Real-Time Tracking in a Surveillance Scenario

arXiv: https://arxiv.org/abs/2106.01153 · HTML: https://mlsanity.com/paper/2106.01153/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 2 Jun 2021.

## Builds on (most cited first, 10 of 10)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Simple Online and Realtime Tracking](https://mlsanity.com/paper/1602.00763/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [Gated Siamese Convolutional Neural Network Architecture for Human Re-Identification](https://mlsanity.com/paper/1607.08378/) (2016)
- [Simple Unsupervised Multi-Object Tracking](https://mlsanity.com/paper/2006.02609/) (2020)
- [Deep Feature Learning with Relative Distance Comparison for Person Re-identification](https://mlsanity.com/paper/1512.03622/) (2015)
- [GCNNMatch: Graph Convolutional Neural Networks for Multi-Object Tracking via Sinkhorn Normalization](https://mlsanity.com/paper/2010.00067/) (2020)
- [Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification](https://mlsanity.com/paper/1912.05949/) (2019)

## Built on by (most cited first, 8 of 8)

- [In Pursuit of Many: A Review of Modern Multiple Object Tracking Systems](https://mlsanity.com/paper/2209.04796/) (2022)
- [Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking](https://mlsanity.com/paper/2402.09865/) (2024)
- [SDOF-Tracker: Fast and Accurate Multiple Human Tracking by Skipped-Detection and Optical-Flow](https://mlsanity.com/paper/2106.14259/) (2021)
- [Engineering an Efficient Object Tracker for Non-Linear Motion](https://mlsanity.com/paper/2407.00738/) (2024)
- [YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID](https://mlsanity.com/paper/2501.13710/) (2025)
- [STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking](https://mlsanity.com/paper/2201.06824/) (2022)
- [Learning Association via Track-Detection Matching for Multi-Object Tracking](https://mlsanity.com/paper/2512.22105/) (2025)
- [Revisiting Multi-Object Tracking Baselines: Hyperparameter Optimization with Multi-Fidelity Greedy Coordinate Search](https://mlsanity.com/paper/2609.12261/) (2026)

## Abstract (arXiv)

This paper presents an approach for tracking in a surveillance scenario. Typical aspects for this scenario are a 24/7 operation with a static camera mounted above the height of a human with many objects or people. The Multiple Object Tracking Benchmark 20 (MOT20) reflects this scenario best. We can show that our approach is real-time capable on this benchmark and outperforms all other real-time capable approaches in HOTA, MOTA, and IDF1. We achieve this by contributing a fast Siamese network reformulated for linear runtime (instead of quadratic) to generate fingerprints from detections. Thus, it is possible to associate the detections to Kalman filters based on multiple tracking specific ratings: Cosine similarity of fingerprints, Intersection over Union, and pixel distance ratio in the image.