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The effect of network coding on error performance in a noisy cooperative relay network Gürültülü i̇sbi̇rli̇kçi̇ telsi̇z röle aǧinda aǧ kodlamasinin başarim üzeri̇ndeki̇ etki̇si̇

Şafak, Ilgin
Yılmaz, Ali Özgür
In this study, a new system for computer vision-based recognition of human actions is presented. The proposed system uses videos as input. The approach is invariant of the location of the action and zoom levels, the appearance of the person, partial occlusions including self-occlusions and some viewpoint changes. It is robust against temporal length variations. Keypoints are tracked through time and the trajectories of tracked keypoints are used for interpreting the human action in the video. Then, features from videos are extracted. A group of features for describing a trajectory are proposed. Trajectories are clustered using these trajectory features. The clustered trajectories are used for describing an image sequence. Image sequence descriptors are the normalized histograms of the clusters of trajectories. At the final stage, the proposed system uses the descriptors of the image sequences in a supervised learning approach.