In this paper, the problem of multi-target tracking with single camera in complex scenes is addressed. A new approach is proposed for multi-target tracking problem that learns from hierarchy of convolution features. First fast Region-based Convolutional Neutral Networks is trained to detect pedestrian in each frame. Then cooperate it with correlation filter tracker which learns target's appearance from pretrained convolutional neural networks. Correlation filter learns from middle and last convolutional layers to enhances targets localization. However correlation filters fail in case of targets full occlusion. This lead to separated tracklets (mini-trajectories) problem. So a post processing step is added to link separated tracklets with minimum-cost network flow. A cost function is used, that depends on motion cues in associating short tracklets. Experimental results on MOT2015 benchmark show that the proposed approach produce comparable result against state-of-the-art approaches. It shows an increase 4.5 % in multiple object tracking accuracy. Also mostly tracked targets is 12.9% vs 7.5% against state-of-the-art minimum-cost network flow tracker.

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Numerous single-image super-resolution algorithms have been proposed in the literature, but few studies address the problem of performance evaluation based on visual perception. While most super-resolution images are evaluated by fullreference metrics, the effectiveness is not clear and the required ground-truth images are not always available in practice. To address these problems, we conduct human subject studies using a large set of super-resolution images and propose a no-reference metric learned from visual perceptual scores. Specifically, we design three types of low-level statistical features in both spatial and frequency domains to quantify super-resolved artifacts, and learn a two-stage regression model to predict the quality scores of super-resolution images without referring to groundtruth images. Extensive experimental results show that the proposed metric is effective and efficient to assess the quality of super-resolution images based on human perception.

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