Human activities include group activities, individual actions, and interactions between objects and people. In computer vision technology, recognizing and categorizing these activities is a vital process. This system will aim at developing a model that can recognize and detect such behavior and apply it in surveillance, health care, military operations, and patient monitoring. In the first place, videos were gathered in order to get a better understanding of the various human activities and interactions. Subsequently, we have converted Video frames into images and pre-processed each image. Characteristic features are extracted from video images by capturing spatial and temporal details. Spatio-temporal interest points using three descriptors Harris STIP, Gabor STIP, and HoG3D STIP are extracted as features. Extracted features are passed to a Heatmap generation process which gives confident key features related to human action. Support Vector Machine (SVM) Classifier is used to analyze these confident key features to label and classify human actions. Various classifier performance metrics, such as accuracy, sensitivity, and specificity, were used to evaluate the performance of the system. Classifiers exhibiting accuracy of around 98.60% served as an indicator of the overall reliability of the proposed system in effectively recognizing human actions.
Keywords
Spatial-Temporal Interest Point, Action Recognition, Multitask Human Action Recognition, Histogram of Oriented Gradients, Harris STIP and Gabor STIP.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Pavankumar Naik and Srinivasa Rao Kunte R;
Methodology: Srinivasa Rao Kunte R;
Software: Pavankumar Naik;
Data Curation: Srinivasa Rao Kunte R;
Writing- Original Draft Preparation: Pavankumar Naik and Srinivasa Rao Kunte R;
Visualization: Srinivasa Rao Kunte R;
Investigation: Pavankumar Naik;
Supervision: Pavankumar Naik and Srinivasa Rao Kunte R;
Validation: Pavankumar Naik;
Writing- Reviewing and Editing: Pavankumar Naik and Srinivasa Rao Kunte R; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr. Srinivasa Rao Kunte R for this research completion and support.
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Pavankumar Naik
Department of Computer Science and Engineering, Institute of Engineering and Technology, Srinivas University, Mangalore, Karnataka, India.
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Cite this article
Pavankumar Naik and Srinivasa Rao Kunte R, “Detection and Recognition of Multitask Human Action Identification from Preloaded Videos Using CCTV Stationary Cameras”, Journal of Machine and Computing, vol.5, no.3, pp. 1518-1531, July 2025, doi: 10.53759/7669/jmc202505120.
