基于深度学习的物体追踪方法综述

Nilesh Uke, Pravin Futane, Neeta Deshpande, Shailaja N. Uke
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引用次数: 0

摘要

在物体跟踪过程中,深度学习算法会跟踪物体的运动,而物体跟踪的主要挑战是估计或预测视频中移动物体的位置和其他相关细节。通常,物体跟踪需要进行物体检测。在计算机视觉应用中,物体的检测、分类和跟踪起着至关重要的作用,而获取有关各种可用技术的信息也具有重要意义。本研究通过分析、总结和研究现有作品,对物体检测技术进行了系统的文献综述。我们从标准期刊中收集了各种最新作品,并在此基础上确定了可用方法、缺点、优点和挑战,同时还提出了研究问题。总体而言,共收集了约 50 篇研究文章,根据各种指标进行的评估显示,大多数文学作品都使用了深度卷积神经网络(Deep CNN),在跟踪物体时,物体检测有助于提高这些网络的性能。本研究还讨论了需要解决的重要问题,这有助于提高物体追踪技术的水平。
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A review on deep learning-based object tracking methods
A deep learning algorithm tracks an object’s movement during object tracking and the main challenge in the tracking of objects is to estimate or forecast the locations and other pertinent details of moving objects in a video. Typically, object tracking entails the process of object detection. In computer vision applications the detection, classification, and tracking of objects play a vital role, and gaining information about the various techniques available also provides significance. In this research, a systematic literature review of the object detection techniques is performed by analyzing, summarizing, and examining the existing works available. Various state of art works are collected from standard journals and the methods available, cons, and pros along with challenges are determined based on this the research questions are also formulated. Overall, around 50 research articles are collected, and the evaluation based on various metrics shows that most of the literary works used Deep convolutional neural networks (Deep CNN), and while tracking the objects object detection helps in enhancing the performance of these networks. The important issues that need to be resolved are also discussed in this research, which helps in leveling up the object-tracking techniques.
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