CGA: a new feature selection model for visual human action recognition
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ORIGINAL ARTICLE
CGA: a new feature selection model for visual human action recognition Ritam Guha1 • Ali Hussain Khan1 • Pawan Kumar Singh2
•
Ram Sarkar1 • Debotosh Bhattacharjee1
Received: 28 January 2020 / Accepted: 12 August 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract Recognition of human actions from visual contents is a budding field of computer vision and image understanding. The problem with such a recognition system is the huge dimensions of the feature vectors. Many of these features are irrelevant to the classification mechanism. For this reason, in this paper, we propose a novel feature selection (FS) model called cooperative genetic algorithm (CGA) to select some of the most important and discriminating features from the entire feature set to improve the classification accuracy as well as the time requirement of the activity recognition mechanism. In CGA, we have made an effort to embed the concepts of cooperative game theory in GA to create a both-way reinforcement mechanism to improve the solution of the FS model. The proposed FS model is tested on four benchmark video datasets named Weizmann, KTH, UCF11, HMDB51, and two sensor-based UCI HAR datasets. The experiments are conducted using four state-of-the-art feature descriptors, namely HOG, GLCM, SURF, and GIST. It is found that there is a significant improvement in the overall classification accuracy while considering very small fraction of the original feature vector. Keywords Human action recognition Cooperative genetic algorithm Feature selection Coalition game Pearson correlation coefficient Weizmann KTH UCF11 HMDB51 UCI HAR
1 Introduction Human action recognition (HAR) plays an essential role in human-to-human interaction and many interpersonal relations by providing vital information about the identity of a person, their personality, and psychological state, which are generally challenging to extract [1]. The ability to & Pawan Kumar Singh [email protected]; [email protected] Ritam Guha [email protected] Ali Hussain Khan [email protected] Ram Sarkar [email protected] Debotosh Bhattacharjee [email protected] 1
Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India
2
Department of Information Technology, Jadavpur University, Kolkata 700106, India
automatically recognizing human activities is one of the leading research fields of computer vision and machine learning. In the field of artificial intelligence, machine learning, and deep learning, researches aiming at understanding human actions received tremendous attention and are monotonically increasing for decades [2, 3]. This, in turn, results in a plethora of HAR techniques proposed by various researchers as well as evaluated on different benchmark datasets containing still images, video sequences, collected from an accelerometer, smartwatch sensors, gyroscope sensors, gravity sensors and also by using a complete length of motion curve
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