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A boosting framework for human posture recognition using spatio-temporal features along with radon transform

  • 1220: Visual and Sensory Data Processing for Real Time Intelligent Surveillance System
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Abstract

Automatic human posture recognition in surveillance videos has real world applications in monitoring old-homes, restoration centers, hospitals, disability, and child-care centers. It also has applications in other areas such as security and surveillance, sports, and abnormal activity recognition. Human posture recognition is a challenging problem due to occlusion, background clutter, illumination variations, camouflage, and noise in the captured video signal. In the current study, which is an extension of our previous work (Ali et al. Sensors, 18(6):1918, 2018), we propose a novel combination of a number of spatio-temporal features computed over human blobs in a temporal window. These features include aspect ratios, shape descriptors, geometric centroids, ellipse axes ratio, silhouette angles, and silhouette speed. In addition to these features, we also exploit the radon transform to get better shape based analysis. In order to obtain improved posture classification accuracy, we used J48 classifier under a boosting framework by employing the AdaBoost algorithm.The proposed algorithm is compared with eighteen existing state-of-the-art approaches on four publicly available datasets including MCF, UR Fall detection, KARD, and NUCLA. Our results demonstrate the excellent performance of the proposed algorithm compared to these existing methods.

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Notes

  1. http://www.iro.umontreal.ca/~labimage/Dataset/

  2. https://data.mendeley.com/datasets/k28dtm7tr6/1

  3. https://users.eecs.northwestern.edu/~jwa368/my_data.html

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Acknowledgements

We are very grateful to Noman Nazar and Reamsha Khan for his support and guidance. We also like to extend our special gratitude to Ms. Sumaira Zafar, Muhammad Junaid and Mahzaib Khalid for their assistance. We are grateful to all the anonymous reviewers for their useful comments. This work was supported by the National ICT R& D under grant no. NICTRDF/NGIRI/2012-13/Corsp/3; and University of Management & Technology, Lahore, Pakistan

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  1. University of Management and Technology, SST, C-II, Johar Town, Lahore, Pakistan

    Salma Aftab, Syed Farooq Ali & Umar Suleman

  2. Information Technology University (ITU), Computer Science, 346-B, Lahore, Pakistan

    Arif Mahmood

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Aftab, S., Ali, S.F., Mahmood, A. et al. A boosting framework for human posture recognition using spatio-temporal features along with radon transform. Multimed Tools Appl 81, 42325–42351 (2022). https://doi.org/10.1007/s11042-022-13536-1

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