research-article

DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions

Authors:

Xiangyang XueAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 2734 - 2743

https://doi.org/10.1145/3581783.3611728

Published: 27 October 2023 Publication History

Abstract

Multiple object tracking (MOT) tends to become more challenging when severe occlusions occur. In this paper, we analyze the limitations of traditional Convolutional Neural Network-based methods and Transformer-based methods in handling occlusions and propose DNMOT, an end-to-end trainable DeNoising Transformer for MOT. To address the challenge of occlusions, we explicitly simulate the scenarios when occlusions occur. Specifically, we augment the trajectory with noises during training and make our model learn the denoising process in an encoder-decoder architecture, so that our model can exhibit strong robustness and perform well under crowded scenes. Additionally, we propose a Cascaded Mask strategy to better coordinate the interaction between different types of queries in the decoder to prevent the mutual suppression between neighboring trajectories under crowded scenes. Notably, the proposed method requires no additional modules like matching strategy and motion state estimation in inference. We conduct extensive experiments on the MOT17, MOT20, and DanceTrack datasets, and the experimental results show that our method outperforms previous state-of-the-art methods by a clear margin.

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Cited By

Li GJian YJian YYan YYan YWang HWang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GLATrack: Global and Local Awareness for Open-Vocabulary Multiple Object TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681530(2457-2466)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681530
Jin HNie XYan YChen XZhu ZQi DCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Object-Level Pseudo-3D Lifting for Distance-Aware TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680783(8015-8023)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680783
Lei YZhu HYuan JXiang GZhong XHe SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DenseTrack: Drone-Based Crowd Tracking via Density-Aware Motion-Appearance SynergyProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680617(2050-2058)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680617
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Index Terms

DeNoising-MOT: Towards Multiple Object Tracking with Severe Occlusions
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Image and video acquisition
        Motion capture

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
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Tao Mei
HiDream.ai, China
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Rita Cucchiara
University of Modena and Reggio Emilia, Italy
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University of Florence, Italy
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Unversidad de Medellin, Colombia
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Pradeep K. Atrey
University at Albany, State University of New York, USA
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M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

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Li GJian YJian YYan YYan YWang HWang HCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)GLATrack: Global and Local Awareness for Open-Vocabulary Multiple Object TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681530(2457-2466)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681530
Jin HNie XYan YChen XZhu ZQi DCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Object-Level Pseudo-3D Lifting for Distance-Aware TrackingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680783(8015-8023)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680783
Lei YZhu HYuan JXiang GZhong XHe SCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)DenseTrack: Drone-Based Crowd Tracking via Density-Aware Motion-Appearance SynergyProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680617(2050-2058)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680617
Wang SJia FMao WLiu YZhao YChen ZWang TZhang CZhang XZhao F(2024)Stream Query Denoising for Vectorized HD-Map ConstructionComputer Vision – ECCV 202410.1007/978-3-031-72655-2_12(203-220)Online publication date: 6-Dec-2024
https://doi.org/10.1007/978-3-031-72655-2_12

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