Enabling deformation slack in tracking with temporally even correlation filters.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Neural Networks Pub Date : 2025-01-01 Epub Date: 2024-10-29 DOI:10.1016/j.neunet.2024.106839
Yuanming Zhang, Huihui Pan, Jue Wang
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引用次数: 0

Abstract

Discriminative correlation filters with temporal regularization have recently attracted much attention in mobile video tracking, due to the challenges of target occlusion and background interference. However, rigidly penalizing the variability of templates between adjacent frames makes trackers lazy for target evolution, leading to inaccurate responses or even tracking failure when deformation occurs. In this paper, we address the problem of instant template learning when the target undergoes drastic variations in appearance and aspect ratio. We first propose a temporally even model featuring deformation slack, which theoretically supports the ability of the template to respond quickly to variations while suppressing disturbances. Then, an optimal derivation of our model is formulated, and the closed form solutions are deduced to facilitate the algorithm implementation. Further, we introduce a cyclic shift methodology for mirror factors to achieve scale estimation of varying aspect ratios, thereby dramatically improving the cross-area accuracy. Comprehensive comparisons on seven datasets demonstrate our excellent performance: DroneTB-70, VisDrone-SOT2019, VOT-2019, LaSOT, TC-128, UAV-20L, and UAVDT. Our approach runs at 16.9 frames per second on a low-cost Central Processing Unit, which makes it suitable for tracking on drones. The code and raw results will be made publicly available at: https://github.com/visualperceptlab/TEDS.

利用时间上均匀的相关滤波器实现跟踪中的变形松弛。
由于面临目标遮挡和背景干扰的挑战,带有时间正则化的判别相关滤波器最近在移动视频跟踪领域引起了广泛关注。然而,硬性惩罚相邻帧之间模板的可变性会使跟踪器懒于考虑目标的演变,导致响应不准确,甚至在发生变形时跟踪失败。在本文中,我们将解决目标在外观和长宽比发生剧烈变化时的即时模板学习问题。我们首先提出了一个具有形变松弛特征的时间均匀模型,从理论上支持模板在抑制干扰的同时快速响应变化的能力。然后,我们对模型进行了优化推导,并推导出了闭式解,以方便算法的实施。此外,我们还引入了镜像因子循环移动方法,以实现不同长宽比的比例估算,从而显著提高了横面积精度。七个数据集的综合比较证明了我们的卓越性能:这些数据集包括:DroneTB-70、VisDrone-SOT2019、VOT-2019、LaSOT、TC-128、UAV-20L 和 UAVDT。我们的方法可在低成本的中央处理器上以每秒 16.9 帧的速度运行,因此适合在无人机上进行跟踪。代码和原始结果将在以下网站公开:https://github.com/visualperceptlab/TEDS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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