Intelligent analysis of surveillance videos over networks requires high recognition accuracy\nby analyzing good-quality videos that however introduce significant bandwidth requirement.\nDegraded video quality because of high object dynamics under wireless video transmission induces\nmore critical issues to the success of smart video surveillance. In this paper, an object-based source\ncoding method is proposed to preserve constant quality of video streaming over wireless networks.\nThe inverse relationship between video quality and object dynamics (i.e., decreasing video quality\ndue to the occurrence of large and fast-moving objects) is characterized statistically as a linear model.\nA regression algorithm that uses robust M-estimator statistics is proposed to construct the linear model\nwith respect to different bitrates. The linear model is applied to predict the bitrate increment required\nto enhance video quality. A simulated wireless environment is set up to verify the proposed method\nunder different wireless situations. Experiments with real surveillance videos of a variety of object\ndynamics are conducted to evaluate the performance of the method. Experimental results demonstrate\nsignificant improvement of streaming videos relative to both visual and quantitative aspects.
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