# SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset

arXiv: https://arxiv.org/abs/2208.02580 · HTML: https://mlsanity.com/paper/2208.02580/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 48 references that are in the map.
- First posted on arXiv 4 Aug 2022.

## Builds on (most cited first, 30 of 30)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [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)
- [ByteTrack: Multi-Object Tracking by Associating Every Detection Box](https://mlsanity.com/paper/2110.06864/) (2021)
- [Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking](https://mlsanity.com/paper/1609.01775/) (2016)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [MOT16: A Benchmark for Multi-Object Tracking](https://mlsanity.com/paper/1603.00831/) (2016)
- [TrackFormer: Multi-Object Tracking with Transformers](https://mlsanity.com/paper/2101.02702/) (2021)
- [FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking](https://mlsanity.com/paper/2004.01888/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [MOTR: End-to-End Multiple-Object Tracking with Transformer](https://mlsanity.com/paper/2105.03247/) (2021)
- [TransTrack: Multiple Object Tracking with Transformer](https://mlsanity.com/paper/2012.15460/) (2020)
- [MOT20: A benchmark for multi object tracking in crowded scenes](https://mlsanity.com/paper/2003.09003/) (2020)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion](https://mlsanity.com/paper/2111.14690/) (2021)
- [Joint Detection and Identification Feature Learning for Person Search](https://mlsanity.com/paper/1604.01850/) (2016)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Person Re-identification in the Wild](https://mlsanity.com/paper/1604.02531/) (2016)
- [MOTChallenge: A Benchmark for Single-Camera Multiple Target Tracking](https://mlsanity.com/paper/2010.07548/) (2020)
- [The EuroCity Persons Dataset: A Novel Benchmark for Object Detection](https://mlsanity.com/paper/1805.07193/) (2018)
- [PANDA: A Gigapixel-level Human-centric Video Dataset](https://mlsanity.com/paper/2003.04852/) (2020)
- [WiderPerson: A Diverse Dataset for Dense Pedestrian Detection in the Wild](https://mlsanity.com/paper/1909.12118/) (2019)
- [The 5th AI City Challenge](https://mlsanity.com/paper/2104.12233/) (2021)
- [PathTrack: Fast Trajectory Annotation with Path Supervision](https://mlsanity.com/paper/1703.02437/) (2017)

## Built on by (most cited first, 3 of 3)

- [Video Understanding with Large Language Models: A Survey](https://mlsanity.com/paper/2312.17432/) (2023)
- [Merlin:Empowering Multimodal LLMs with Foresight Minds](https://mlsanity.com/paper/2312.00589/) (2023)
- [The Impact of Detector Architecture, Model Scale, and Scene Density on Multi-Pedestrian Tracking](https://mlsanity.com/paper/2610.19369/) (2026)

## Abstract (arXiv)

Multi-object tracking (MOT) has been dominated by the use of track by detection approaches due to the success of convolutional neural networks (CNNs) on detection in the last decade. As the datasets and bench-marking sites are published, research direction has shifted towards yielding best accuracy on generic scenarios including re-identification (reID) of objects while tracking. In this study, we narrow the scope of MOT for surveillance by providing a dedicated dataset of pedestrians and focus on in-depth analyses of well performing multi-object trackers to observe the weak and strong sides of state-of-the-art (SOTA) techniques for real-world applications. For this purpose, we introduce SOMPT22 dataset; a new set for multi person tracking with annotated short videos captured from static cameras located on poles with 6-8 meters in height positioned for city surveillance. This provides a more focused and specific benchmarking of MOT for outdoor surveillance compared to public MOT datasets. We analyze MOT trackers classified as one-shot and two-stage with respect to the way of use of detection and reID networks on this new dataset. The experimental results of our new dataset indicate that SOTA is still far from high efficiency, and single-shot trackers are good candidates to unify fast execution and accuracy with competitive performance. The dataset will be available at: sompt22.github.io