PolyTrack: Tracking with Bounding Polygons

• URL: http://arxiv.org/abs/2111.01606v1
• Date: Tue, 2 Nov 2021 14:05:41 GMT
• Title: PolyTrack: Tracking with Bounding Polygons
• Authors: Gaspar Faure and Hughes Perreault and Guillaume-Alexandre Bilodeau and Nicolas Saunier
• Abstract summary: Polytrack detects objects by producing heatmaps of their center keypoint. rough segmentation is done by computing a bounding polygon over each instance instead of the traditional bounding box. We trained and evaluated PolyTrack on the MOTS and KITTIMOTS datasets.
• Score: 11.365829102707014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Learning Data Association for Multi-Object Tracking using Only Coordinates [8.786893621311433]
  Transformer-based module TWiX is trained on sets of tracks with the objective of discriminating pairs of tracks coming from the same object from those which are not. By inserting TWiX within an online cascade matching pipeline, our tracker C-TWiX achieves state-of-the-art performance on the DanceTrack and KITTIMOT datasets.
  arXiv  Detail & Related papers  (2024-03-12T18:36:18Z)
• Progressive Evolution from Single-Point to Polygon for Scene Text [79.29097971932529]
  We introduce Point2Polygon, which can efficiently transform single-points into compact polygons. Our method uses a coarse-to-fine process, starting with creating anchor points based on recognition confidence, then vertically and horizontally refining the polygon. In training detectors with polygons generated by our method, we attained 86% of the accuracy relative to training with ground truth (GT); 3) Additionally, the proposed Point2Polygon can be seamlessly integrated to empower single-point spotters to generate polygons.
  arXiv  Detail & Related papers  (2023-12-21T12:08:27Z)
• Dense Optical Tracking: Connecting the Dots [82.79642869586587]
  DOT is a novel, simple and efficient method for solving the problem of point tracking in a video. We show that DOT is significantly more accurate than current optical flow techniques, outperforms sophisticated "universal trackers" like OmniMotion, and is on par with, or better than, the best point tracking algorithms like CoTracker.
  arXiv  Detail & Related papers  (2023-12-01T18:59:59Z)
• CoTracker: It is Better to Track Together [70.63040730154984]
  CoTracker is a transformer-based model that tracks a large number of 2D points in long video sequences. We show that joint tracking significantly improves tracking accuracy and robustness, and allows CoTracker to track occluded points and points outside of the camera view.
  arXiv  Detail & Related papers  (2023-07-14T21:13:04Z)
• DPPD: Deformable Polar Polygon Object Detection [3.9236649268347765]
  We develop a novel Deformable Polar Polygon Object Detection method (DPPD) to detect objects in polygon shapes. DPPD has been demonstrated successfully in various object detection tasks for autonomous driving.
  arXiv  Detail & Related papers  (2023-04-05T06:43:41Z)
• ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box [81.45219802386444]
  Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. We propose a hierarchical data association strategy to mine the true objects in low-score detection boxes. In 3D scenarios, it is much easier for the tracker to predict object velocities in the world coordinate.
  arXiv  Detail & Related papers  (2023-03-27T15:35:21Z)
• DIVOTrack: A Novel Dataset and Baseline Method for Cross-View Multi-Object Tracking in DIVerse Open Scenes [74.64897845999677]
  We introduce a new cross-view multi-object tracking dataset for DIVerse Open scenes with dense tracking pedestrians. Our DIVOTrack has fifteen distinct scenarios and 953 cross-view tracks, surpassing all cross-view multi-object tracking datasets currently available. Furthermore, we provide a novel baseline cross-view tracking method with a unified joint detection and cross-view tracking framework named CrossMOT.
  arXiv  Detail & Related papers  (2023-02-15T14:10:42Z)
• CenterPoly: real-time instance segmentation using bounding polygons [11.365829102707014]
  We present a novel method, called CenterPoly, for real-time instance segmentation using bounding polygons. We apply it to detect road users in dense urban environments, making it suitable for applications in intelligent transportation systems like automated vehicles. Most of the network parameters are shared by the network heads, making it fast and lightweight enough to run at real-time speed.
  arXiv  Detail & Related papers  (2021-08-19T21:31:30Z)
• Polygonal Point Set Tracking [50.445151155209246]
  We propose a novel learning-based polygonal point set tracking method. Our goal is to track corresponding points on the target contour. We present visual-effects applications of our method on part distortion and text mapping.
  arXiv  Detail & Related papers  (2021-05-30T17:12:36Z)
• PointTrackNet: An End-to-End Network For 3-D Object Detection and Tracking From Point Clouds [13.174385375232161]
  We propose PointTrackNet, an end-to-end 3-D object detection and tracking network. It generates foreground masks, 3-D bounding boxes, and point-wise tracking association displacements for each detected object.
  arXiv  Detail & Related papers  (2020-02-26T15:19:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.