RETRACTED: Basketball action recognition based on FPGA and particle image

Abstract

Fine-grained motion recognition is most important for such video retrieval, and most work nowadays focuses on coarse-grained and fine-grained actions in motion recognition, without being involved in many uses. To solve this problem, in this system, it have a dataset that challenged a basketball game by annotating detailed actions in a video. Adaptive Multi-Label Classification methods for basketball action recognition benchmark also provides data about the system. In addition, this method is proposed to integrate the FPGA into a network of two data streams in order to find the finest areas of basketball action recognition and extracts the features of the recognition system. This proposed system gives significantly a better and superior results than the existing methods. Taken individually, the surrounding first-person footage can be associated with similar situations in the past and compared with the visual semantics of the spatial and social layout of personal records. In general, first-person videos can track common interests, and can be linked to group of individuals in this system.