Multi-camera joint spatial self-organization for intelligent interconnection surveillance. (January 2022)

• Record Type: Journal Article
• Authors: Li, Congcong; Li, Jing; Xie, Yuguang; Nie, Jiayang; Yang, Tao; Lu, Zhaoyang
• Is Part Of: Engineering applications of artificial intelligence. Volume 107(2022)
• Publication Date: 2022-01
• Subjects: Intelligent interconnection surveillance -- Multi-camera spatial self-organization -- Multi-camera joint optimization -- Sequence complementary integration; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104533
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: The construction of smart city makes information interconnection play an increasingly important role in intelligent surveillance systems. Especially the interconnection among massive cameras is the key to realizing the evolution from current fragmented monitoring to interconnection surveillance. However, it remains a challenging problem in practical systems due to large sensor quantity, various camera types, and complex spatial layout. Aimed at this problem, this paper proposes a novel multi-camera joint spatial self-organization approach, which realizes interconnection surveillance by unifying cameras into one imaging space. Differing from existing back-end data association strategy, our method takes front-end data calibration as a breakthrough to relate surveillance data. Specifically, this paper first initials camera spatial parameter by sequence complementary feature integration. Through integrating complementarity and redundancy among sequence features, our method has robustness under scene dynamic changes and noise. Then, we propose a multi-camera joint optimization method based on common monitoring coverage correlation analysis to estimate a more accurate relative relationship. By leveraging the two strategies, the spatial relationship and visual data association across monitoring cameras are returned finally. Our system organizes all cameras into a unified imaging space by itself. Extensive experimental evaluations on an actual campus environment demonstrate our method achieves remarkable performance. Highlights: Automatic spatial relationship mining between multiple scattered cameras. The key technology from fragmented monitoring to interconnection surveillance. Starting with front end to promote existing multi-camera monitoring integration. Multi-camera joint optimization based on common monitoring data correlation. Sequence complementary feature integration for camera spatial initialization.