# Tracking the Trackers: An Analysis of the State of the Art in Multiple Object Tracking

arXiv: https://arxiv.org/abs/1704.02781 · HTML: https://mlsanity.com/paper/1704.02781/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2017.

## Built on by (most cited first, 18 of 18)

- [SG-Net: Spatial Granularity Network for One-Stage Video Instance Segmentation](https://mlsanity.com/paper/2103.10284/) (2021)
- [The Multi-Temporal Urban Development SpaceNet Dataset](https://mlsanity.com/paper/2102.04420/) (2021)
- [Learning a Proposal Classifier for Multiple Object Tracking](https://mlsanity.com/paper/2103.07889/) (2021)
- [The SpaceNet Multi-Temporal Urban Development Challenge](https://mlsanity.com/paper/2102.11958/) (2021)
- [Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs](https://mlsanity.com/paper/2102.04091/) (2021)
- [Context-Aware Streaming Perception in Dynamic Environments](https://mlsanity.com/paper/2208.07479/) (2022)
- [Bridging Images and Videos: A Simple Learning Framework for Large Vocabulary Video Object Detection](https://mlsanity.com/paper/2212.10147/) (2022)
- [Tracking by Associating Clips](https://mlsanity.com/paper/2212.10149/) (2022)
- [Does Video Compression Impact Tracking Accuracy?](https://mlsanity.com/paper/2202.00892/) (2022)
- [A two-stage data association approach for 3D Multi-object Tracking](https://mlsanity.com/paper/2101.08684/) (2021)
- [RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety](https://mlsanity.com/paper/2102.11730/) (2021)
- [Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/2112.07664/) (2021)
- [Persistent Animal Identification Leveraging Non-Visual Markers](https://mlsanity.com/paper/2112.06809/) (2021)
- [MOTCOM: The Multi-Object Tracking Dataset Complexity Metric](https://mlsanity.com/paper/2207.10031/) (2022)
- [Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment](https://mlsanity.com/paper/2408.05940/) (2024)
- [Contour Errors: Ego-Centric Matching for 3D Multi-Object Tracking Performance Evaluation](https://mlsanity.com/paper/2506.04122/) (2025)
- [Marine Bubble Flow Quantification Using Wide-Baseline Stereo Photogrammetry](https://mlsanity.com/paper/2112.07414/) (2021)
- [Deep MDP: A Modular Framework for Multi-Object Tracking](https://mlsanity.com/paper/2310.14294/) (2023)

## Abstract (arXiv)

Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. We present a benchmark for Multiple Object Tracking launched in the late 2014, with the goal of creating a framework for the standardized evaluation of multiple object tracking methods. This paper collects the two releases of the benchmark made so far, and provides an in-depth analysis of almost 50 state-of-the-art trackers that were tested on over 11000 frames. We show the current trends and weaknesses of multiple people tracking methods, and provide pointers of what researchers should be focusing on to push the field forward.