18 February 2019 Deep learning-based scene-awareness approach for intelligent change detection in videos
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Abstract
Change detection is the most fundamental component in video surveillance systems. Although many change detection approaches have been proposed, they are often only suitable for particular environments. This paper presents an approach that integrates several detection techniques with scene understanding capability, thereby overcoming the challenges of detecting various scene types and improving overall detection performance. First, a scene-awareness algorithm that incorporates a deep learning-based scene recognition model and support vector machine is developed to classify the monitored scene over time. Then, the appropriate detection technique is automatically adopted to perform scene-specific detection. Experimental results demonstrate that the performance of the proposed method is comparable to that of the state-of-the-art methods and satisfies the requirements of real-time practical applications. Hence, it can serve as an intelligent change detection approach for visual analytics in video surveillance systems.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Yi-Tung Chan "Deep learning-based scene-awareness approach for intelligent change detection in videos," Journal of Electronic Imaging 28(1), 013038 (18 February 2019). https://doi.org/10.1117/1.JEI.28.1.013038
Received: 25 August 2018; Accepted: 28 January 2019; Published: 18 February 2019
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CITATIONS
Cited by 8 scholarly publications.
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KEYWORDS
Video

Video surveillance

Sensors

Scene classification

Detection and tracking algorithms

Environmental sensing

Fermium

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