ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking

• URL: http://arxiv.org/abs/2211.03919v1
• Date: Tue, 8 Nov 2022 00:20:38 GMT
• Title: ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking
• Authors: Tara Sadjadpour, Jie Li, Rares Ambrus, and Jeannette Bohg
• Abstract summary: Multi-object tracking is a cornerstone capability of any robotic system. Detectors function in a low precision recall regime, ensuring a low number of false-negatives while producing a high rate of false-positives. We propose a method that learns shape andtemporal affinities between consecutive frames to better distinguish between true-positive and false-positive detections and tracks.
• Score: 25.093138732809738
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Multi-object tracking is a cornerstone capability of any robotic system. Most approaches follow a tracking-by-detection paradigm. However, within this framework, detectors function in a low precision-high recall regime, ensuring a low number of false-negatives while producing a high rate of false-positives. This can negatively affect the tracking component by making data association and track lifecycle management more challenging. Additionally, false-negative detections due to difficult scenarios like occlusions can negatively affect tracking performance. Thus, we propose a method that learns shape and spatio-temporal affinities between consecutive frames to better distinguish between true-positive and false-positive detections and tracks, while compensating for false-negative detections. Our method provides a probabilistic matching of detections that leads to robust data association and track lifecycle management. We quantitatively evaluate our method through ablative experiments and on the nuScenes tracking benchmark where we achieve state-of-the-art results. Our method not only estimates accurate, high-quality tracks but also decreases the overall number of false-positive and false-negative tracks. Please see our project website for source code and demo videos: sites.google.com/view/shasta-3d-mot/home.

Related papers

• RTracker: Recoverable Tracking via PN Tree Structured Memory [71.05904715104411]
  We propose a recoverable tracking framework, RTracker, that uses a tree-structured memory to dynamically associate a tracker and a detector to enable self-recovery. Specifically, we propose a Positive-Negative Tree-structured memory to chronologically store and maintain positive and negative target samples. Our core idea is to use the support samples of positive and negative target categories to establish a relative distance-based criterion for a reliable assessment of target loss.
  arXiv  Detail & Related papers  (2024-03-28T08:54:40Z)
• SCTracker: Multi-object tracking with shape and confidence constraints [11.210661553388615]
  This paper proposes a multi-object tracker based on shape constraint and confidence named SCTracker. Intersection of Union distance with shape constraints is applied to calculate the cost matrix between tracks and detections. The Kalman Filter based on the detection confidence is used to update the motion state to improve the tracking performance when the detection has low confidence.
  arXiv  Detail & Related papers  (2023-05-16T15:18:42Z)
• QDTrack: Quasi-Dense Similarity Learning for Appearance-Only Multiple Object Tracking [73.52284039530261]
  We present Quasi-Dense Similarity Learning, which densely samples hundreds of object regions on a pair of images for contrastive learning. We find that the resulting distinctive feature space admits a simple nearest neighbor search at inference time for object association. We show that our similarity learning scheme is not limited to video data, but can learn effective instance similarity even from static input.
  arXiv  Detail & Related papers  (2022-10-12T15:47:36Z)
• CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion [34.42289908350286]
  3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection. It can be challenging to accurately track the irregular motion of objects for LiDAR-based methods. We propose a novel camera-LiDAR fusion 3D MOT framework based on the Combined Appearance-Motion Optimization (CAMO-MOT)
  arXiv  Detail & Related papers  (2022-09-06T14:41:38Z)
• A Bayesian Detect to Track System for Robust Visual Object Tracking and Semi-Supervised Model Learning [1.7268829007643391]
  We ad-dress problems in a Bayesian tracking and detection framework parameterized by neural network outputs. We propose a particle filter-based approximate sampling algorithm for tracking object state estimation. Based on our particle filter inference algorithm, a semi-supervised learn-ing algorithm is utilized for learning tracking network on intermittent labeled frames.
  arXiv  Detail & Related papers  (2022-05-05T00:18:57Z)
• Unsupervised Noisy Tracklet Person Re-identification [100.85530419892333]
  We present a novel selective tracklet learning (STL) approach that can train discriminative person re-id models from unlabelled tracklet data. This avoids the tedious and costly process of exhaustively labelling person image/tracklet true matching pairs across camera views. Our method is particularly more robust against arbitrary noisy data of raw tracklets therefore scalable to learning discriminative models from unconstrained tracking data.
  arXiv  Detail & Related papers  (2021-01-16T07:31:00Z)
• Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking [83.75789829291475]
  We introduce a probabilistic autoregressive motion model to score tracklet proposals. This is achieved by training our model to learn the underlying distribution of natural tracklets. Our experiments demonstrate the superiority of our approach at tracking objects in challenging sequences.
  arXiv  Detail & Related papers  (2020-12-03T23:59:27Z)
• Uncertainty-Aware Voxel based 3D Object Detection and Tracking with von-Mises Loss [13.346392746224117]
  Uncertainty helps us tackle the error in the perception system and improve robustness. We propose a method for improving target tracking performance by adding uncertainty regression to the SECOND detector.
  arXiv  Detail & Related papers  (2020-11-04T21:53:31Z)
• Cascaded Regression Tracking: Towards Online Hard Distractor Discrimination [202.2562153608092]
  We propose a cascaded regression tracker with two sequential stages. In the first stage, we filter out abundant easily-identified negative candidates. In the second stage, a discrete sampling based ridge regression is designed to double-check the remaining ambiguous hard samples.
  arXiv  Detail & Related papers  (2020-06-18T07:48:01Z)
• ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking [80.02322563402758]
  One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets. We introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion.
  arXiv  Detail & Related papers  (2020-04-16T06:43:11Z)

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.