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Although promising results have been achieved in the areas of object detection and classification, few works have provided an end-to-end solution to the perception problems in the autonomous driving field. In this paper, we make two contributions. Firstly, we fully enhanced our previously released TT100K benchmark and provide 16,817 elaborately labeled Tencent Street View panoramas.

This paper addresses the problem of color reduction which aims at computing a compact representation of a color coordinate
system. By capitalizing on studies that have suggested the existence of eleven focal colors, we conducted subjective
experiments which exploited the categorical nature of human color perception. This paper describes a novel color reduction

Real-time identification of facial expressions is an important topic in the area of human computer interaction and pattern recognition. The research has gained significant attention in recent years. However, many challenges still exist. This is because an individual might display different expressions at different times even for the same mood. Expressions can also be influenced by health. Our proposed framework aims to capture unique information related to facial expressions from salient patches.

The existing single depth image super-resolution (SR)
methods suppose that the image to be interpolated is noise
free. However, the supposition is invalid in practice because
noise will be inevitably introduced in the depth image acquisition
process. In this paper, we address the problem of image
denoising and SR jointly based on designing sparse graphs
that are useful for describing the geometric structures of data
domains. In our method, we first cluster similar patches in a
noisy depth image and compute an average patch. Different

License plate detection is a challenging task when dealing with open environments and images captured from a certain distance by lowcost cameras. In this paper, we propose an approach for detecting license plates based on a convolutional neural network which models a function that produces a score for each image sub-region, allowing us to estimate the locations of the detected license plates by combining the results obtained from sparse overlapping regions.

In this paper, we propose complex form of local orientation plane (Comp-LOP) for object tracking. Comp-LOP is a simple but effective descriptor, which is robust to occlusion for object tracking. It effectively considers spatiotemporal relationship between the target and its surrounding regions in a correlation filter framework by the complex form, which successfully deals with the heavy occlusion problem. Moreover, scale estimation is performed to treat target scale variations for improving tracking accuracy.