The wireless visual sensor network (WVSN) as a new emerged intelligent visual system, has been applied in many video monitoring sites. However, there is still great challenge because of the limited wireless network bandwidth. To resolve the problem, we propose a real-time dynamic texture approach which can detect and reduce the temporal redundancy during many successive image frames. Firstly, an adaptively learning background model is improved to discover successive similar image frames from the inputting video sequence. Then, the dynamic texture model based on the singular value decomposition is adopted to distinguish foreground and background element dynamics. Furthermore, a background discarding strategy based on visual motion coherence is proposed to determine whether each image frame is streamed or not. To evaluate the trade-off performance of the proposed method, it is tested on the CDW-2014 dataset, which can accurately detect the first foreground frame when the moving objects of interest appear in the field of view in the most tested dynamic scenes, and the misdetection rate of the undetected foreground frames is near to zero. Compared to the original stream, it can reduce the occupied bandwidth a lot and its computational cost is relatively lower than the state-of-the-art methods.
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