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.

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