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A Post-Processing Approach in Moving Objects Detection via Feature Pyramid Networks

Recent work has shown that Convolutional Neural Networks (CNNs) have great ability to deal with classification problems in pattern recognition field. Moving objects detection, regarding as a classification process, labels every pixel as a foreground pixel or a background pixel. In this paper, we proposed an effective post-processing approach, Residual Background Networks (ResBGNets), to improve the accuracy of moving objects detection in video sequences. Instead of learning the ground truth directly, our model learns the residual pictures between the results of existing methods and the ground truth. It benefits to understand the hidden character of each algorithm and correct the misclassification. Inside ResBGNets, we build Feature Pyramid Networks (FPN) to combine spatial information of the low-resolution level with semantical features of high-level of the high-resolution level. Evaluation performed on the 2014 CDnet dataset reveals that through our approach, most of the existing background subtraction methods can get better detection results and a significant higher FM score.