New Generation Deep Learning for Video Object Detection: A Survey

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 p...

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Vydáno v:IEEE transaction on neural networks and learning systems Ročník 33; číslo 8; s. 3195 - 3215
Hlavní autoři: Jiao, Licheng, Zhang, Ruohan, Liu, Fang, Yang, Shuyuan, Hou, Biao, Li, Lingling, Tang, Xu
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.08.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3053249