# PathTrack: Fast Trajectory Annotation with Path Supervision

arXiv: https://arxiv.org/abs/1703.02437 · HTML: https://mlsanity.com/paper/1703.02437/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2017.

## Built on by (most cited first, 20 of 20)

- [TransMOT: Spatial-Temporal Graph Transformer for Multiple Object Tracking](https://mlsanity.com/paper/2104.00194/) (2021)
- [TransCenter: Transformers with Dense Representations for Multiple-Object Tracking](https://mlsanity.com/paper/2103.15145/) (2021)
- [Towards Discriminative Representation: Multi-view Trajectory Contrastive Learning for Online Multi-object Tracking](https://mlsanity.com/paper/2203.14208/) (2022)
- [Self-Supervised Multi-Object Tracking with Cross-Input Consistency](https://mlsanity.com/paper/2111.05943/) (2021)
- [Adiabatic Quantum Computing for Multi Object Tracking](https://mlsanity.com/paper/2202.08837/) (2022)
- [Large Scale Real-World Multi-Person Tracking](https://mlsanity.com/paper/2211.02175/) (2022)
- [DG-Labeler and DGL-MOTS Dataset: Boost the Autonomous Driving Perception](https://mlsanity.com/paper/2110.07790/) (2021)
- [CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2311.00987/) (2023)
- [Recent Advances in Embedding Methods for Multi-Object Tracking: A Survey](https://mlsanity.com/paper/2205.10766/) (2022)
- [SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow](https://mlsanity.com/paper/2404.11426/) (2024)
- [Video Annotation for Visual Tracking via Selection and Refinement](https://mlsanity.com/paper/2108.03821/) (2021)
- [SOMPT22: A Surveillance Oriented Multi-Pedestrian Tracking Dataset](https://mlsanity.com/paper/2208.02580/) (2022)
- [ReaMOT: A Benchmark and Framework for Reasoning-based Multi-Object Tracking](https://mlsanity.com/paper/2505.20381/) (2025)
- [UTOPIA: Unconstrained Tracking Objects without Preliminary Examination via Cross-Domain Adaptation](https://mlsanity.com/paper/2306.09613/) (2023)
- [Mixture of Dynamical Variational Autoencoders for Multi-Source Trajectory Modeling and Separation](https://mlsanity.com/paper/2312.04167/) (2023)
- [Heuristics2Annotate: Efficient Annotation of Large-Scale Marathon Dataset For Bounding Box Regression](https://mlsanity.com/paper/2104.02749/) (2021)
- [Unsupervised Multiple-Object Tracking with a Dynamical Variational Autoencoder](https://mlsanity.com/paper/2202.09315/) (2022)
- [UnsMOT: Unified Framework for Unsupervised Multi-Object Tracking with Geometric Topology Guidance](https://mlsanity.com/paper/2309.01078/) (2023)
- [Collecting Consistently High Quality Object Tracks with Minimal Human Involvement by Using Self-Supervised Learning to Detect Tracker Errors](https://mlsanity.com/paper/2405.03643/) (2024)
- [Efficient Retail Video Annotation: A Robust Key Frame Generation Approach for Product and Customer Interaction Analysis](https://mlsanity.com/paper/2506.14854/) (2025)

## Abstract (arXiv)

Progress in Multiple Object Tracking (MOT) has been historically limited by the size of the available datasets. We present an efficient framework to annotate trajectories and use it to produce a MOT dataset of unprecedented size. In our novel path supervision the annotator loosely follows the object with the cursor while watching the video, providing a path annotation for each object in the sequence. Our approach is able to turn such weak annotations into dense box trajectories. Our experiments on existing datasets prove that our framework produces more accurate annotations than the state of the art, in a fraction of the time. We further validate our approach by crowdsourcing the PathTrack dataset, with more than 15,000 person trajectories in 720 sequences. Tracking approaches can benefit training on such large-scale datasets, as did object recognition. We prove this by re-training an off-the-shelf person matching network, originally trained on the MOT15 dataset, almost halving the misclassification rate. Additionally, training on our data consistently improves tracking results, both on our dataset and on MOT15. On the latter, we improve the top-performing tracker (NOMT) dropping the number of IDSwitches by 18% and fragments by 5%.