Deep learning in multi-object detection and tracking: state of the art

Object detection and tracking is one of the most important and challenging branches in computer vision, and have been widely applied in various fields, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With the rapid development of deep learning (DL) networks and GPU’...

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Bibliographic Details
Published inApplied intelligence (Dordrecht, Netherlands) Vol. 51; no. 9; pp. 6400 - 6429
Main Authors Pal, Sankar K., Pramanik, Anima, Maiti, J., Mitra, Pabitra
Format Journal Article
LanguageEnglish
Published New York Springer US 01.09.2021
Springer Nature B.V
Subjects
30th Anniversary Special Issue
Anomalies
Artificial Intelligence
Computation
Computer Science
Computer vision
Deep learning
Granulation
Machines
Manufacturing
Mechanical Engineering
Meteorological satellites
Object recognition
Processes
Telematics
Tracking
Granular computing
Deep learning (DL)
Object tracking
Video analysis
Object detection
Machine learning
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Summary:Object detection and tracking is one of the most important and challenging branches in computer vision, and have been widely applied in various fields, such as health-care monitoring, autonomous driving, anomaly detection, and so on. With the rapid development of deep learning (DL) networks and GPU’s computing power, the performance of object detectors and trackers has been greatly improved. To understand the main development status of object detection and tracking pipeline thoroughly, in this survey, we have critically analyzed the existing DL network-based methods of object detection and tracking and described various benchmark datasets. This includes the recent development in granulated DL models. Primarily, we have provided a comprehensive overview of a variety of both generic object detection and specific object detection models. We have enlisted various comparative results for obtaining the best detector, tracker, and their combination. Moreover, we have listed the traditional and new applications of object detection and tracking showing its developmental trends. Finally, challenging issues, including the relevance of granular computing, in the said domain are elaborated as a future scope of research, together with some concerns. An extensive bibliography is also provided.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-021-02293-7