FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking

• URL: http://arxiv.org/abs/2409.07904v1
• Date: Thu, 12 Sep 2024 10:14:48 GMT
• Title: FACT: Feature Adaptive Continual-learning Tracker for Multiple Object Tracking
• Authors: Rongzihan Song, Zhenyu Weng, Huiping Zhuang, Jinchang Ren, Yongming Chen, Zhiping Lin,
• Abstract summary: We propose a new MOT framework called the Feature Adaptive Continual-learning Tracker (FACT) FACT enables real-time tracking and feature learning for targets by utilizing all past tracking information. We demonstrate that the framework can be integrated with various state-of-the-art feature-based trackers.
• Score: 22.53374351982883
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multiple object tracking (MOT) involves identifying multiple targets and assigning them corresponding IDs within a video sequence, where occlusions are often encountered. Recent methods address occlusions using appearance cues through online learning techniques to improve adaptivity or offline learning techniques to utilize temporal information from videos. However, most existing online learning-based MOT methods are unable to learn from all past tracking information to improve adaptivity on long-term occlusions while maintaining real-time tracking speed. On the other hand, temporal information-based offline learning methods maintain a long-term memory to store past tracking information, but this approach restricts them to use only local past information during tracking. To address these challenges, we propose a new MOT framework called the Feature Adaptive Continual-learning Tracker (FACT), which enables real-time tracking and feature learning for targets by utilizing all past tracking information. We demonstrate that the framework can be integrated with various state-of-the-art feature-based trackers, thereby improving their tracking ability. Specifically, we develop the feature adaptive continual-learning (FAC) module, a neural network that can be trained online to learn features adaptively using all past tracking information during tracking. Moreover, we also introduce a two-stage association module specifically designed for the proposed continual learning-based tracking. Extensive experiment results demonstrate that the proposed method achieves state-of-the-art online tracking performance on MOT17 and MOT20 benchmarks. The code will be released upon acceptance.

Related papers

• FeatureSORT: Essential Features for Effective Tracking [0.0]
  We introduce a novel tracker designed for online multiple object tracking with a focus on being simple, while being effective. By integrating distinct appearance features, including clothing color, style, and target direction, our tracker significantly enhances online tracking accuracy.
  arXiv  Detail & Related papers  (2024-07-05T04:37:39Z)
• COOLer: Class-Incremental Learning for Appearance-Based Multiple Object Tracking [32.47215340215641]
  This paper extends the scope of continual learning research to class-incremental learning for multiple object tracking (MOT) Previous solutions for continual learning of object detectors do not address the data association stage of appearance-based trackers. We introduce COOLer, a COntrastive- and cOntinual-Learning-based tracker, which incrementally learns to track new categories while preserving past knowledge.
  arXiv  Detail & Related papers  (2023-10-04T17:49:48Z)
• 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)
• Context-aware Visual Tracking with Joint Meta-updating [11.226947525556813]
  We propose a context-aware tracking model to optimize the tracker over the representation space, which jointly meta-update both branches by exploiting information along the whole sequence. The proposed tracking method achieves an EAO score of 0.514 on VOT2018 with the speed of 40FPS, demonstrating its capability of improving the accuracy and robustness of the underlying tracker with little speed drop.
  arXiv  Detail & Related papers  (2022-04-04T14:16:00Z)
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data [101.6195176510611]
  "Online" continual learning enables evaluating both information retention and online learning efficacy. In online continual learning, each incoming small batch of data is first used for testing and then added to the training set, making the problem truly online. We introduce a new benchmark for online continual visual learning that exhibits large scale and natural distribution shifts.
  arXiv  Detail & Related papers  (2021-08-20T06:17:20Z)
• Crop-Transform-Paste: Self-Supervised Learning for Visual Tracking [137.26381337333552]
  In this work, we develop the Crop-Transform-Paste operation, which is able to synthesize sufficient training data. Since the object state is known in all synthesized data, existing deep trackers can be trained in routine ways without human annotation.
  arXiv  Detail & Related papers  (2021-06-21T07:40:34Z)
• 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 3D Multi-Modal, Multi-Object Tracking for Autonomous Driving [22.693895321632507]
  We propose a probabilistic, multi-modal, multi-object tracking system consisting of different trainable modules. We show that our method outperforms current state-of-the-art on the NuScenes Tracking dataset.
  arXiv  Detail & Related papers  (2020-12-26T15:00:54Z)
• Meta-learning the Learning Trends Shared Across Tasks [123.10294801296926]
  Gradient-based meta-learning algorithms excel at quick adaptation to new tasks with limited data. Existing meta-learning approaches only depend on the current task information during the adaptation. We propose a 'Path-aware' model-agnostic meta-learning approach.
  arXiv  Detail & Related papers  (2020-10-19T08:06:47Z)
• Unsupervised Deep Representation Learning for Real-Time Tracking [137.69689503237893]
  We propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning.
  arXiv  Detail & Related papers  (2020-07-22T08:23:12Z)
• Robust Visual Object Tracking with Two-Stream Residual Convolutional Networks [62.836429958476735]
  We propose a Two-Stream Residual Convolutional Network (TS-RCN) for visual tracking. Our TS-RCN can be integrated with existing deep learning based visual trackers. To further improve the tracking performance, we adopt a "wider" residual network ResNeXt as its feature extraction backbone.
  arXiv  Detail & Related papers  (2020-05-13T19:05:42Z)

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.