New Generation Deep Learning for Video Object Detection: A Survey

• Published in: IEEE transaction on neural networks and learning systems Vol. 33; no. 8; pp. 3195 - 3215
• Main Authors: Jiao, Licheng, Zhang, Ruohan, Liu, Fang, Yang, Shuyuan, Hou, Biao, Li, Lingling, Tang, Xu
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Computer vision; Convolution; Deep learning; Detection algorithms; Feature extraction; Learning; Learning systems; Machine learning; Meteorological satellites; neural networks; Object detection; Object recognition; pipeline processing; Surveys; Task analysis; Telematics; Video data; video signal processing
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2021.3053249

Video object detection, a basic task in the computer vision field, is rapidly evolving and widely used. In recent years, deep learning methods have rapidly become widespread in the field of video object detection, achieving excellent results compared with those of traditional methods. However, the presence of duplicate information and abundant spatiotemporal information in video data poses a serious challenge to video object detection. Therefore, in recent years, many scholars have investigated deep learning detection algorithms in the context of video data and have achieved remarkable results. Considering the wide range of applications, a comprehensive review of the research related to video object detection is both a necessary and challenging task. This survey attempts to link and systematize the latest cutting-edge research on video object detection with the goal of classifying and analyzing video detection algorithms based on specific representative models. The differences and connections between video object detection and similar tasks are systematically demonstrated, and the evaluation metrics and video detection performance of nearly 40 models on two data sets are presented. Finally, the various applications and challenges facing video object detection are discussed.