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Recently, very deep two-stream ConvNets have achieved great discriminative power for video classification, which is especially the case for the temporal ConvNets when trained on multi-frame optical flow. However, action recognition in videos often fall prey to the wild camera motion, which poses challenges on the extraction of reliable optical flow for human body. In light of this, we propose a novel...
Image class segmentation is a problem that combines image segmentation and image classification. Conditional random field can be used in image class segmentation to achieve state-of-the-art result, adding high-level information in the course of using low-level cues to conduct segmentation. In this paper we introduce a method using weighted neighborhood histogram on the over-segmented original images...
Mean-shift is an effective algorithm for object tracking. However, it has a poor performance when the illumination condition changes fast or the tracking target being shadowed. By contract, particle filter based object tracking has a better tracking performance, but the tracking speed is much slower compared to mean-shift. Owing to the limitations of just using a single algorithm, a novel object tracking...
In this paper, the problem of foreground segmentation in videos is considered. The bag of superpixels is proposed to simultaneously model both the foreground and the background. Then it is demonstrated that an image has a hierarchical structure. Based on this observation, the discriminative nature of sparse representations is exploited to segment the foreground in each frame. Experimental results...
In this paper, we present an intelligent fusion framework to combine two complementary features for visual tracking. Color histogram has attracted much attention in recent years due to its simplicity and robustness. The capability of color histogram to deal with partial occlusion and non-rigidity has been demonstrated. In practice, however, relying on color information only is not sufficient in cases...
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