On integration of multiple features for human activity recognition in video sequences
| العنوان: | On integration of multiple features for human activity recognition in video sequences |
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| المؤلفون: | Prashant K. Srivastava, Ashish Khare, Arati Kushwaha |
| المصدر: | Multimedia Tools and Applications. 80:32511-32538 |
| بيانات النشر: | Springer Science and Business Media LLC, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Discrete wavelet transform, Computer Networks and Communications, Local binary patterns, business.industry, Computer science, Deep learning, Feature vector, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Activity recognition, Support vector machine, Histogram of oriented gradients, Hardware and Architecture, Media Technology, Feature (machine learning), Artificial intelligence, business, Software |
| الوصف: | Human activity recognition has become one of the most active areas of research in computer vision, due to its increasing demand in many automated monitoring applications such as visual surveillance, human-computer interaction, health care, security systems, and many more. This work aims to introduce an integrated feature descriptor which combines texture feature and shape feature, at multiple orientations, to construct the efficient and robust feature vector for activity recognition in realistic scenarios. This feature descriptor is an integration of Discrete Wavelet Transform (DWT), multiscale Local Binary Pattern, and Histogram of Oriented Gradients (HOG). HOG descriptor extracts local-oriented histograms of the frame sequences, multiscale LBP gives the complex structural information of the frames and DWT gives the directional information at multiple scales. By exploiting these properties, we have constructed an integrated feature descriptor to construct the feature vector and achieves promising results of activity recognition in realistic videos. Multiclass Support Vector Machine (SVM) classifier with one-vs-one architecture has been used for activity recognition. The experiments are performed on five benchmark publicly available video datasets, namely Weizmann, IXMAS, UT Interaction, HMDB51, and UCF101. The experimental results are compared with the results of other state-of-art methods based on conventional machine learning and deep learning-based methods to show the effectiveness and usefulness of the proposed work. The experimental results have demonstrated that the proposed method performs better than the other state-of-art methods. |
| تدمد: | 1573-7721 1380-7501 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::891493948de5770a1861d7b647369a67 https://doi.org/10.1007/s11042-021-11207-1 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........891493948de5770a1861d7b647369a67 |
| قاعدة البيانات: | OpenAIRE |
Find this article in full text from Springer Verlag. | تدمد: | 15737721 13807501 |
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