Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow
| العنوان: | Human Activity Recognition Algorithm in Video Sequences Based on Integration of Magnitude and Orientation Information of Optical Flow |
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| المؤلفون: | Arati Kushwaha, Manish Khare, Ashish Khare |
| المصدر: | International Journal of Image and Graphics. 22 |
| بيانات النشر: | World Scientific Pub Co Pte Ltd, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Orientation (computer vision), Computer science, business.industry, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Optical flow, Magnitude (mathematics), Video sequence, Computer Graphics and Computer-Aided Design, Computer Science Applications, Activity recognition, Support vector machine, Histogram of oriented gradients, Computer vision, Computer Vision and Pattern Recognition, Artificial intelligence, Surveillance monitoring, business |
| الوصف: | Human activity recognition from video sequences has emerged recently as pivotal research area due to its importance in a large number of applications such as real-time surveillance monitoring, healthcare, smart homes, security, behavior analysis, and many more. However, lots of challenges also exist such as intra-class variations, object occlusion, varying illumination condition, complex background, camera motion, etc. In this work, we introduce a novel feature descriptor based on the integration of magnitude and orientation information of optical flow and histogram of oriented gradients which gives an efficient and robust feature vector for the recognition of human activities for real-world environment. In the proposed approach first we computed magnitude and orientation of the optical flow separately then a local-oriented histogram of magnitude and orientation of motion flow vectors are computed using histogram of oriented gradients followed by linear combination feature fusion strategy. The resultant features are then processed by a multiclass Support Vector Machine (SVM) classifier for activity recognition. The experimental results are performed over different publically available benchmark video datasets such as UT interaction, CASIA, and HMDB51 datasets. The effectiveness of the proposed approach is evaluated in terms of six different performance parameters such as accuracy, precision, recall, specificity, [Formula: see text]-measure, and Matthew’s correlation coefficient (MCC). To show the significance of the proposed method, it is compared with the other state-of-the-art methods. The experimental result shows that the proposed method performs well in comparison to other state-of-the-art methods. |
| تدمد: | 1793-6756 0219-4678 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::2665349abd5441e5dd46934e6864e04e https://doi.org/10.1142/s0219467822500097 |
| رقم الأكسشن: | edsair.doi...........2665349abd5441e5dd46934e6864e04e |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 17936756 02194678 |
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