# PolyTrack: Tracking with Bounding Polygons

arXiv: https://arxiv.org/abs/2111.01606 · HTML: https://mlsanity.com/paper/2111.01606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 2 Nov 2021.

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

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [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)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [Tracking Objects as Points](https://mlsanity.com/paper/2004.01177/) (2020)
- [Tracking without bells and whistles](https://mlsanity.com/paper/1903.05625/) (2019)
- [MOTS: Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/1902.03604/) (2019)
- [ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation](https://mlsanity.com/paper/2012.05258/) (2020)
- [EagerMOT: 3D Multi-Object Tracking via Sensor Fusion](https://mlsanity.com/paper/2104.14682/) (2021)
- [Learning by tracking: Siamese CNN for robust target association](https://mlsanity.com/paper/1604.07866/) (2016)
- [Spatial-Temporal Relation Networks for Multi-Object Tracking](https://mlsanity.com/paper/1904.11489/) (2019)
- [Do Different Tracking Tasks Require Different Appearance Models?](https://mlsanity.com/paper/2107.02156/) (2021)
- [Track to Reconstruct and Reconstruct to Track](https://mlsanity.com/paper/1910.00130/) (2019)
- [Deep Network Flow for Multi-Object Tracking](https://mlsanity.com/paper/1706.08482/) (2017)
- [Beyond Pixels: Leveraging Geometry and Shape Cues for Online Multi-Object Tracking](https://mlsanity.com/paper/1802.09298/) (2018)
- [Segment as Points for Efficient Online Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2007.01550/) (2020)
- [Accurate Single Stage Detector Using Recurrent Rolling Convolution](https://mlsanity.com/paper/1704.05776/) (2017)
- [ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2007.03200/) (2020)
- [CenterPoly: real-time instance segmentation using bounding polygons](https://mlsanity.com/paper/2108.08923/) (2021)
- [Tracking Multiple Moving Objects Using Unscented Kalman Filtering Techniques](https://mlsanity.com/paper/1802.01235/) (2018)
- [Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector](https://mlsanity.com/paper/1809.02073/) (2018)
- [Online Multi-Object Tracking and Segmentation with GMPHD Filter and Mask-based Affinity Fusion](https://mlsanity.com/paper/2009.00100/) (2020)

## Abstract (arXiv)

In this paper, we present a novel method called PolyTrack for fast multi-object tracking and segmentation using bounding polygons. Polytrack detects objects by producing heatmaps of their center keypoint. For each of them, a rough segmentation is done by computing a bounding polygon over each instance instead of the traditional bounding box. Tracking is done by taking two consecutive frames as input and computing a center offset for each object detected in the first frame to predict its location in the second frame. A Kalman filter is also applied to reduce the number of ID switches. Since our target application is automated driving systems, we apply our method on urban environment videos. We trained and evaluated PolyTrack on the MOTS and KITTIMOTS datasets. Results show that tracking polygons can be a good alternative to bounding box and mask tracking. The code of PolyTrack is available at https://github.com/gafaua/PolyTrack.