# So you think you can track?

arXiv: https://arxiv.org/abs/2309.07268 · HTML: https://mlsanity.com/paper/2309.07268/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 81 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 24 of 24)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (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)
- [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)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (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)
- [Multiple Object Tracking: A Literature Review](https://mlsanity.com/paper/1409.7618/) (2014)
- [CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification](https://mlsanity.com/paper/1903.09254/) (2019)
- [Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking](https://mlsanity.com/paper/2007.14557/) (2020)
- [UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking](https://mlsanity.com/paper/1511.04136/) (2015)
- [MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries](https://mlsanity.com/paper/2205.00613/) (2022)
- [I-24 MOTION: An instrument for freeway traffic science](https://mlsanity.com/paper/2301.11198/) (2023)
- [Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model](https://mlsanity.com/paper/2105.01213/) (2021)
- [Transformer-Based Sensor Fusion for Autonomous Driving: A Survey](https://mlsanity.com/paper/2302.11481/) (2023)
- [Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities](https://mlsanity.com/paper/2208.14167/) (2022)
- [Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with overlapping FOVs](https://mlsanity.com/paper/2102.04091/) (2021)
- [Automatic vehicle trajectory data reconstruction at scale](https://mlsanity.com/paper/2212.07907/) (2022)
- [The Interstate-24 3D Dataset: a new benchmark for 3D multi-camera vehicle tracking](https://mlsanity.com/paper/2308.14833/) (2023)
- [Multi-Camera Sensor Fusion for Visual Odometry using Deep Uncertainty Estimation](https://mlsanity.com/paper/2112.12818/) (2021)

## Built on by (most cited first, 5 of 5)

- [Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test](https://mlsanity.com/paper/2402.17050/) (2024)
- [Enhancing Vehicle Re-identification and Matching for Weaving Analysis](https://mlsanity.com/paper/2407.04688/) (2024)
- [(U)NFV: Supervised and Unsupervised Neural Finite Volume Methods for Solving Hyperbolic PDEs](https://mlsanity.com/paper/2505.23702/) (2025)
- [Noise-Aware Generative Microscopic Traffic Simulation](https://mlsanity.com/paper/2508.07453/) (2025)
- [Explainable Forensics of Manipulated Segments in Untrimmed Long Videos](https://mlsanity.com/paper/2606.02402/) (2026)

## Abstract (arXiv)

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding.