{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T14:17:39Z","timestamp":1753885059771,"version":"3.41.2"},"reference-count":33,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","funder":[{"name":"Doctoral Research Fund Project of Zhongshan Polytechnic","award":["KYG2103"],"award-info":[{"award-number":["KYG2103"]}]},{"name":"Universities in Guangdong Province","award":["2021KTSCX315"],"award-info":[{"award-number":["2021KTSCX315"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Image Grap."],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:p> Human action recognition in videos is an important task in computer vision with applications in fields such as surveillance, human\u2013computer interaction, and sports analysis. However, it is a challenging task due to the complex background changes and redundancy of long-term video information. In this paper, we propose a novel bi-directional long short-term memory method with attention pooling based on joint motion and difference entropy (JEAP-BiLSTM) to address these challenges. To obtain discriminative features, we introduce a joint entropy map that measures both the entropy of motion and the entropy of change. The Bi-LSTM method is then applied to capture visual and temporal associations in both forward and backward directions, enabling efficient capture of long-term temporal correlation. Furthermore, attention pooling is used to highlight the region of interest and to mitigate the effects of background changes in video information. Experiments on the UCF101 and HMDB51 datasets demonstrate that the proposed JEAP-BiLSTM method achieves recognition rates of 96.4% and 75.2%, respectively, outperforming existing methods. Our proposed method makes significant contributions to the field of human action recognition by effectively capturing both spatial and temporal patterns in videos, addressing background changes, and achieving state-of-the-art performance. <\/jats:p>","DOI":"10.1142\/s0219467825500184","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T05:51:20Z","timestamp":1691128280000},"source":"Crossref","is-referenced-by-count":0,"title":["A Jeap-BiLSTM Neural Network for Action Recognition"],"prefix":"10.1142","volume":"25","author":[{"given":"Lunzheng","family":"Tan","sequence":"first","affiliation":[{"name":"School of Information Engineering, Zhongshan Polytechnic, No. 25 BoAi Road, Zhongshan, Guangdong 528400, P. R. China"}]},{"given":"Yanfei","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Zhongshan Polytechnic, No. 25 BoAi Road, Zhongshan, Guangdong 528400, P. R. China"}]},{"given":"Limin","family":"Xia","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Central South University, Changsha, Hunan 410075, P. R. China"}]},{"given":"Shangsheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Guangdong Polytechnic Institute, No. 3 Dangui Road, Wugui Mountain Area, Zhongshan City, Guangdong Province, P. R. China"}]},{"given":"Zhanben","family":"Zhou","sequence":"additional","affiliation":[{"name":"Guangdong Polytechnic Institute, No. 3 Dangui Road, Wugui Mountain Area, Zhongshan City, Guangdong Province, P. R. 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