Review on Video Object Tracking Based on Deep Learning

• 作者: Biruk Assefa Fangming Bi Huayi Chen Jingru Li Wei Chen Weidong Fang Xin Ma
• 来源期刊: 新媒体杂志(英文)
• 学科: 工学
• 关键词: OBJECT TRACKING DEEP learning NEURAL WORK
• 年，卷（期）: 2019,（2）
• 页码范围: 63-74
• 页数: 12页
• 分类号: TP3

摘要

Video object tracking is an important research topic of computer vision, whichfinds a wide range of applications in video surveillance, robotics, human-computerinteraction and so on. Although many moving object tracking algorithms have beenproposed, there are still many difficulties in the actual tracking process, such asillumination change, occlusion, motion blurring, scale change, self-change and so on.Therefore, the development of object tracking technology is still challenging. Theemergence of deep learning theory and method provides a new opportunity for theresearch of object tracking, and it is also the main theoretical framework for the researchof moving object tracking algorithm in this paper. In this paper, the existing deeptracking-based target tracking algorithms are classified and sorted out. Based on theprevious knowledge and my own understanding, several solutions are proposed for theexisting methods. In addition, the existing deep learning target tracking method is stilldifficult to meet the requirements of real-time, how to design the network and trackingprocess to achieve speed and effect improvement, there is still a lot of research space.