TY - JOUR ID - 125084 TI - Human Action Recognition using Prominent Camera JO - International Journal of Engineering JA - IJE LA - en SN - 1025-2495 AU - Kavimandan, P. Sh. AU - Kapoor, R. AU - Yadav, K. AD - Indira Gandhi Delhi Technical University for Women, Delhi, India AD - Delhi Technological University, Delhi, India Y1 - 2021 PY - 2021 VL - 34 IS - 2 SP - 427 EP - 432 KW - action recognition KW - Prominent camera KW - Support Vector Machine KW - Modified Bag-of-words DO - 10.5829/ije.2021.34.02b.14 N2 - Human action recognition has undoubtedly been under research for a long time. The reason being its vast applications such as visual surveillance, security, video retrieval, human interaction with machine/robot in the entertainment sector, content-based video compression, and many more. Multiple cameras are used to overcome human action recognition challenges such as occlusion and variation in viewpoint. The use of multiple cameras overloads the system with a large amount of data, thus a good recognition rate is achieved with cost (in terms of both computation and data) as the overhead. In this research, we propose a methodology to improve the action recognition rate by using a single camera from multiple camera environments. We applied a modified bag-of-visual-words based action recognition method with the Radial Basis Function-Support Vector Machine (RBF-SVM) as a classifier. Our experiment on a standard and publicly available dataset with multiple cameras shows an improved recognition rate compared to other state-of-the-art methods. UR - https://www.ije.ir/article_125084.html L1 - https://www.ije.ir/article_125084_8a379f5597aca8ab8ad2856e60097bab.pdf ER -