Object detection from dynamic scene using joint background modeling and fast deep learning classification

In this paper, we couple effective dynamic background modeling with fast deep learning classification to develop an accurate scheme for human-animal detection from camera-trap images with cluttered moving objects. We introduce a new block-wise background model, named as Minimum Feature Difference (MFD), to model the variation of the background of the camera-trap sequences and generate the foreground object proposals. We then develop a region proposals verification to reduce the number of false alarms. Finally, we perform complexity-accuracy analysis of DCNN to construct a fast deep learning classification scheme to classify these region proposals into three categories: human, animals, and background patches. The optimized DCNN is able to maintain high level of accuracy while reducing the computational complexity by 14 times, which allows near real-time implementation of the proposed method on CPU machines. Our experimental results demonstrate that the proposed method outperforms existing methods on our and Alexander von Humboldt Institute camera-trap datasets in both foreground segmentation and object detection.