Multi-Object Tracking by Hierarchical Visual Representations

• URL: http://arxiv.org/abs/2402.15895v1
• Date: Sat, 24 Feb 2024 20:10:44 GMT
• Title: Multi-Object Tracking by Hierarchical Visual Representations
• Authors: Jinkun Cao, Jiangmiao Pang, Kris Kitani
• Abstract summary: We propose a new visual hierarchical representation paradigm for multi-object tracking. It is more effective to discriminate between objects by attending to objects' compositional visual regions and contrasting with the background contextual information.
• Score: 40.521291165765696
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new visual hierarchical representation paradigm for multi-object tracking. It is more effective to discriminate between objects by attending to objects' compositional visual regions and contrasting with the background contextual information instead of sticking to only the semantic visual cue such as bounding boxes. This compositional-semantic-contextual hierarchy is flexible to be integrated in different appearance-based multi-object tracking methods. We also propose an attention-based visual feature module to fuse the hierarchical visual representations. The proposed method achieves state-of-the-art accuracy and time efficiency among query-based methods on multiple multi-object tracking benchmarks.

Related papers

• VOVTrack: Exploring the Potentiality in Videos for Open-Vocabulary Object Tracking [61.56592503861093]
  This issue amalgamates the complexities of open-vocabulary object detection (OVD) and multi-object tracking (MOT) Existing approaches to OVMOT often merge OVD and MOT methodologies as separate modules, predominantly focusing on the problem through an image-centric lens. We propose VOVTrack, a novel method that integrates object states relevant to MOT and video-centric training to address this challenge from a video object tracking standpoint.
  arXiv  Detail & Related papers  (2024-10-11T05:01:49Z)
• ResVG: Enhancing Relation and Semantic Understanding in Multiple Instances for Visual Grounding [42.10086029931937]
  Visual grounding aims to localize the object referred to in an image based on a natural language query. Existing methods demonstrate a significant performance drop when there are multiple distractions in an image. We propose a novel approach, the Relation and Semantic-sensitive Visual Grounding (ResVG) model, to address this issue.
  arXiv  Detail & Related papers  (2024-08-29T07:32:01Z)
• Multi-Granularity Language-Guided Multi-Object Tracking [95.91263758294154]
  We propose a new multi-object tracking framework, named LG-MOT, that explicitly leverages language information at different levels of granularity. At inference, our LG-MOT uses the standard visual features without relying on annotated language descriptions. Our LG-MOT achieves an absolute gain of 2.2% in terms of target object association (IDF1 score) compared to the baseline using only visual features.
  arXiv  Detail & Related papers  (2024-06-07T11:18:40Z)
• Contextual Object Detection with Multimodal Large Language Models [66.15566719178327]
  We introduce a novel research problem of contextual object detection. Three representative scenarios are investigated, including the language cloze test, visual captioning, and question answering. We present ContextDET, a unified multimodal model that is capable of end-to-end differentiable modeling of visual-language contexts.
  arXiv  Detail & Related papers  (2023-05-29T17:50:33Z)
• On the robustness of self-supervised representations for multi-view object classification [0.0]
  We show that self-supervised representations based on the instance discrimination objective lead to better representations of objects that are more robust to changes in the viewpoint and perspective of the object. We find that self-supervised representations are more robust to object viewpoint and appear to encode more pertinent information about objects that facilitate the recognition of objects from novel views.
  arXiv  Detail & Related papers  (2022-07-27T17:24:55Z)
• Multi-layer Feature Aggregation for Deep Scene Parsing Models [19.198074549944568]
  In this paper, we explore the effective use of multi-layer feature outputs of the deep parsing networks for spatial-semantic consistency. The proposed module can auto-select the intermediate visual features to correlate the spatial and semantic information. Experiments on four public scene parsing datasets prove that the deep parsing network equipped with the proposed feature aggregation module can achieve very promising results.
  arXiv  Detail & Related papers  (2020-11-04T23:07:07Z)
• Learning to Compose Hypercolumns for Visual Correspondence [57.93635236871264]
  We introduce a novel approach to visual correspondence that dynamically composes effective features by leveraging relevant layers conditioned on the images to match. The proposed method, dubbed Dynamic Hyperpixel Flow, learns to compose hypercolumn features on the fly by selecting a small number of relevant layers from a deep convolutional neural network.
  arXiv  Detail & Related papers  (2020-07-21T04:03:22Z)
• Visual Tracking by TridentAlign and Context Embedding [71.60159881028432]
  We propose novel TridentAlign and context embedding modules for Siamese network-based visual tracking methods. The performance of the proposed tracker is comparable to that of state-of-the-art trackers, while the proposed tracker runs at real-time speed.
  arXiv  Detail & Related papers  (2020-07-14T08:00:26Z)
• MOPT: Multi-Object Panoptic Tracking [33.77171216778909]
  We introduce a novel perception task denoted as multi-object panoptic tracking (MOPT) MOPT allows for exploiting pixel-level semantic information of 'thing' and'stuff' classes, temporal coherence, and pixel-level associations over time. We present extensive quantitative and qualitative evaluations of both vision-based and LiDAR-based MOPT that demonstrate encouraging results.
  arXiv  Detail & Related papers  (2020-04-17T11:45:28Z)

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.