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Human activity recognition in egocentric video using HOG, GiST and color features

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Abstract

With the rapid increase in digital technology, most research areas are involved in human activity recognition, which can help to analyze the activities of patients. A novel approach for human activity recognition in egocentric video has been invoked in this research article. Generally, only the objects are identified, but the actions are not recognized. With this motivation and new trends, this paper presents an efficient technique to recognize the activities. In our approach, first the various activity dataset is trained, and the feature vector values are stored for various activities, which are applied to the testing inputs. Here, we use a filtering technique, i.e., a median filter followed by a segmentation method using watershed and feature extraction, such as a Histogram of Oriented Gradient (HOG), Color and GiST and a combination of all Features. Features are reduced using a genetic algorithm, and classification is done using Support Vector Machine (SVM) and a Random Forest classifier. The experimental results demonstrate that the Random Forest classifier outperformed the SVM classifier.

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Authors and Affiliations

  1. Department of ECE, Annamalai University, Chidambaram, India

    K. P. Sanal Kumar

  2. Department of CSE, Annamalai University, Chidambaram, India

    R. Bhavani

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  1. K. P. Sanal Kumar
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  2. R. Bhavani
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Correspondence to K. P. Sanal Kumar.

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Sanal Kumar, K.P., Bhavani, R. Human activity recognition in egocentric video using HOG, GiST and color features. Multimed Tools Appl 79, 3543–3559 (2020). https://doi.org/10.1007/s11042-018-6034-1

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  • DOI: https://doi.org/10.1007/s11042-018-6034-1

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