A-DQRBRL: attention based deep Q reinforcement battle royale learning model for sports video classification

Abstract

ABSTRACT Analyzing video content and classification are the major challenges for computer vision applications. Thus, the proposed research develops a new attention-based deep Q reinforcement battle royale learning (A-DQRBRL) model for effective sports video classification. Initially, the required keyframes from the input sports video are extracted by an action template approach. Then, pre-processing is performed to remove unwanted noises through cross-guided bilateral filter (CGBF). From the pre-processed frames, the most discriminative features are extracted by Elman recurrent neural network (ERNN). Then, the needed portions are segmented from the extracted features by utilizing a cascaded random forest (CRF) approach. Finally, the sports video is classified by proposing a new A-DQRBRL method. The experimental setup is done by python tool, and the proposed work attains an accuracy of 99.29% in the SVW dataset and 98.97% in the UCF101 dataset. Thus, the experimental results show that the proposed techniques perform better than previous methods.