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Compared with traditional video surveillance systems, wireless video sensor systems are more suitable for emergency application scenarios, such as underground coal mine disaster rescue, due to their low power consumption and rapid deployment. Considering the limited computing power and transmission bandwidth of video sensor nodes, we propose an adaptive compression and hybrid multiple hypothesis based residual reconstruction algorithm based on normalized Bhattacharyya coefficient (NBCAC-MHRR) to solve the high efficiency video coding (HEVC) problem in underground coal mines. First, a low-complexity adaptive sampling rate allocation method is performed on the encoding side. Second, by integrating the background subtraction idea, we combine the high-quality reconstruction of the foreground with the multi-hypothesis residual reconstruction of the background to improve the overall reconstruction effect of the video sequence. Simulation results show that the proposed algorithm can achieve higher reconstruction quality and efficiency than previous methods, especially in underground coal mine application scenarios.

An Adaptive Distributed Compressed Video Sensing Algorithm Based on Normalized Bhattacharyya Coefficient for Coal Mine Monitoring Video