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

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.

KEYWORDS:

• Key frame extraction
• pre-processing
• Elman recurrent neurak network
• cascaded random forest
• deep Q-learning
• sports video classification
• cross guided bilateral filter
• spatial and temporal features
• segmentation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.