ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking

• URL: http://arxiv.org/abs/2004.07482v1
• Date: Thu, 16 Apr 2020 06:43:11 GMT
• Title: ArTIST: Autoregressive Trajectory Inpainting and Scoring for Tracking
• Authors: Fatemeh Saleh, Sadegh Aliakbarian, Mathieu Salzmann, Stephen Gould
• Abstract summary: 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.
• Score: 80.02322563402758
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the core components in online multiple object tracking (MOT) frameworks is associating new detections with existing tracklets, typically done via a scoring function. Despite the great advances in MOT, designing a reliable scoring function remains a challenge. In this paper, we introduce a probabilistic autoregressive generative model to score tracklet proposals by directly measuring the likelihood that a tracklet represents natural motion. One key property of our model is its ability to generate multiple likely futures of a tracklet given partial observations. This allows us to not only score tracklets but also effectively maintain existing tracklets when the detector fails to detect some objects even for a long time, e.g., due to occlusion, by sampling trajectories so as to inpaint the gaps caused by misdetection. Our experiments demonstrate the effectiveness of our approach to scoring and inpainting tracklets on several MOT benchmark datasets. We additionally show the generality of our generative model by using it to produce future representations in the challenging task of human motion prediction.

Related papers

• Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking [52.04679257903805]
  Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks.
  arXiv  Detail & Related papers  (2024-07-19T07:48:45Z)
• BASE: Probably a Better Approach to Multi-Object Tracking [0.0]
  Probabilistic tracking algorithms, which are leading in other fields, are surprisingly absent from the leaderboards. We present BASE, a simple, performant and easily extendible visual tracker, achieving state-of-the-art (SOTA) on MOT17 and MOT20, without using Re-Id.
  arXiv  Detail & Related papers  (2023-09-21T12:58:21Z)
• TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses [51.60422927416087]
  3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. We present TrajectoryFormer, a novel point-cloud-based 3D MOT framework.
  arXiv  Detail & Related papers  (2023-06-09T13:31:50Z)
• MotionTrack: Learning Robust Short-term and Long-term Motions for Multi-Object Tracking [56.92165669843006]
  We propose MotionTrack, which learns robust short-term and long-term motions in a unified framework to associate trajectories from a short to long range. For dense crowds, we design a novel Interaction Module to learn interaction-aware motions from short-term trajectories, which can estimate the complex movement of each target. For extreme occlusions, we build a novel Refind Module to learn reliable long-term motions from the target's history trajectory, which can link the interrupted trajectory with its corresponding detection.
  arXiv  Detail & Related papers  (2023-03-18T12:38:33Z)
• DEFT: Detection Embeddings for Tracking [3.326320568999945]
  We propose an efficient joint detection and tracking model named DEFT. Our approach relies on an appearance-based object matching network jointly-learned with an underlying object detection network. DEFT has comparable accuracy and speed to the top methods on 2D online tracking leaderboards.
  arXiv  Detail & Related papers  (2021-02-03T20:00:44Z)
• 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)
• Tracklets Predicting Based Adaptive Graph Tracking [51.352829280902114]
  We present an accurate and end-to-end learning framework for multi-object tracking, namely textbfTPAGT. It re-extracts the features of the tracklets in the current frame based on motion predicting, which is the key to solve the problem of features inconsistent.
  arXiv  Detail & Related papers  (2020-10-18T16:16:49Z)
• RetinaTrack: Online Single Stage Joint Detection and Tracking [22.351109024452462]
  We focus on the tracking-by-detection paradigm for autonomous driving where both tasks are mission critical. We propose a conceptually simple and efficient joint model of detection and tracking, called RetinaTrack, which modifies the popular single stage RetinaNet approach.
  arXiv  Detail & Related papers  (2020-03-30T23:46:29Z)
• Unsupervised Multiple Person Tracking using AutoEncoder-Based Lifted Multicuts [11.72025865314187]
  We present an unsupervised multiple object tracking approach based on minimum visual features and lifted multicuts. We show that, despite being trained without using the provided annotations, our model provides competitive results on the challenging MOT Benchmark for pedestrian tracking.
  arXiv  Detail & Related papers  (2020-02-04T09:42:34Z)

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.