Hong Kong Applied Science and Technology Research Institute Company, Limited

Man Yau Chiu

Zhi Bin Lei

Yang Liu

Hailiang Li

Yan Wang

Kangheng Wu

Methods and systems which provide super-resolution synthesis based on weighted results from a random forest classifier are described. Embodiments apply a trained random forest classifier to low-resolution patches generated from the low-resolution input image to classify the low-resolution input patches. As each low-resolution patch is fed into the random forest classifier, each decision tree in the random forest classifier 'votes' for a particular class for each of the low-resolution patches. Each class is associated with a projection matrix. The projection matrices output by the decision trees are combined by a weighted average to calculate an overall projection matrix corresponding to the random forest classifier output, which is used to calculate a high-resolution patch for each low-resolution patch. The high-resolution patches are combined to generate a synthesized high-resolution image corresponding to the low-resolution input image.

Systems and methods for super-resolution synthesis based on weighted results from a random forest classifier

A method for a vehicle management system with a perspective view camera generating perspective images of vehicles in a traffic direction, including performing an anti-perspective transform on the perspective images; separating each anti-perspective image into sub-images along one of a radial direction and a tangential direction relative to the traffic direction; determining a scale factor for each sub-image based upon measuring a scale of each vehicle at plural positions in one image using a frame difference method performing a scale transform for each sub-image using the corresponding scale factors for the sub-image; combining each of the scale transformed sub-images for each vehicle into corresponding enhanced anti-perspective images; performing vehicle detection for each vehicle based a combination of the enhanced anti-perspective images in which tracking is enhanced with an optimized detection box size range determined by the enhanced anti-perspective images; and performing vehicle tracking for each vehicle based on the combination of the enhanced anti-perspective images in which detecting is enhanced with the optimized detection box size range determined by the enhanced anti-perspective images.

Vehicle detection, tracking and localization based on enhanced anti-perspective transformation

This invention discloses an encoding apparatus and a decoding apparatus for scalable video coding such that a non-scalable video decoder is usable for decoding a scalable video bit-stream comprising a base layer bit-stream and an enhancement layer bit-stream. In one embodiment, a source video frame is downscaled to give a downscaled video frame, which is then encoded into the base layer bit-stream. The difference between the source video frame and an up-scaled video frame reconstructed from the downscaled video frame in the base layer bit-stream yields a residual frame. The residual frame is partitioned into a number of residual sub-frames each having a resolution that is the downscaled video frame's resolution. The residual sub-frames are encoded into the enhancement layer bit-stream. Thereby, a non-scalable encoder is usable to encode both the downscaled video frame and the residual sub-frames, allowing both bit-streams to be decodable by employing only one non-scalable decoder.

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Hong Kong, China

Man Yau Chiu