Thermal and visible videos can provide complementary information in the moving object detection and recognition. However, most previous approaches focus on the detection and recognition of moving objects from visible videos. In this paper, we present a two-stage approach to moving object recognition by jointly utilizing the thermal and visible videos. In the first stage, we extract the static appearance and the optical flow of moving objects from both sources of videos based on deep networks and generate the bounding box proposals of moving objects. In this stage, two sources of video frames need to be first registered to cover the same scenes with the same resolution. In the second stage, we design a deep network to recognize the categories of the object proposals generated in the first stage and thus obtain the recognition results. Combining the thermal and visible information for recognizing moving objects can improve the performance especially in the low light conditions. To evaluate the proposed approach, we build a thermal-visible video dataset consisting of 200 video pairs. Experimental results demonstrate the effectiveness of the proposed approach.
Skin detection plays an important role in many applications, including face detection, human motion analysis, and objectionable image filtering. We propose a novel skin detection approach named multiple Gaussian models (MGMs). This approach combines multiple single Gaussian models and determines each model in order to maximize the true positive rate (TPR) of skin detection subject to a fixed predefined false positive rate (FPR). We derive the discrete and continuous forms of MGM approaches in the paper. The proposed approach has almost optimal performance for a broad range of FPRs in the Gaussian framework. Moreover, it has low computational costs in skin detection for new image instances. Experimental results show that the MGM approach has better skin detection performance than previous methods within the Gaussian framework.
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