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Describing the contents of images is a challenging task for machines to achieve. It requires not only accurate recognition of objects and humans, but also their attributes and relationships as well as scene information. It would be even more challenging to extend this process to identify falls and hazardous objects to aid elderly or users in need of care. This research makes initial attempts to deal...
Action parsing in videos with complex scenes is an interesting but challenging task in computer vision. In this paper, we propose a novel deep model based on 3D CNN (convolutional neural network) and LSTM (long short-term memory) module with a multi-task learning manner for effective Deep Action Parsing (DAP3D-Net) in videos. Particularly in the training phase, each action clip, sliced to several...
Algorithms for enhancement of low quality compressed videos are described and evaluated in this paper. Cascaded and combined spatio-temporal filters using hierarchical motion estimation and occlusion area detection are investigated for removal of severe coding artifacts and temporal flickering. Both objective and subjective evaluations prove that temporal filtering can significantly improve the video...
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