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A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition

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Intelligent Computing: Image Processing Based Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1157))

Abstract

Human action recognition (HAR) has been a well-studied research topic in the field of computer vision since the past two decades. The objective of HAR is to detect and recognize actions performed by one or more persons based on a series of observations. In this paper, a comparative study of different feature descriptors applied for HAR on video datasets is presented. In particular, we estimate four standard feature descriptors, namely histogram of oriented gradients (HOG), gray-level co-occurrence matrix (GLCM), speeded-up robust features (SURF), and Graphics and Intelligence-based Scripting Technology (GIST) descriptors from RGB videos, after performing background subtraction and creating a minimum bounding box surrounding the human object. To further speed up the overall process, we apply an efficient sparse filtering method, which reduces the number of features by eliminating the redundant features and assigning weights to the features left after elimination. Finally, the performance of the said feature descriptors on three standard benchmark video datasets namely, KTH, HMDB51, and UCF11 has been analyzed.

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Author information

Authors and Affiliations

  1. Department of Information Technology, Indian Institute of Engineering Science and Technology, Howrah-711103, West Bengal, India

    Swarnava Sadhukhan & Siddhartha Mallick

  2. Department of Information Technology, Jadavpur University, Kolkata-700106, West Bengal, India

    Pawan Kumar Singh

  3. Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, West Bengal, India

    Ram Sarkar & Debotosh Bhattacharjee

Authors
  1. Swarnava Sadhukhan
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  2. Siddhartha Mallick
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  3. Pawan Kumar Singh
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  4. Ram Sarkar
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  5. Debotosh Bhattacharjee
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Corresponding author

Correspondence to Pawan Kumar Singh .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Electronics and Communication Engineering, University of Engineering and Management, Kolkata, West Bengal, India

    Soumen Banerjee

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Sadhukhan, S., Mallick, S., Singh, P.K., Sarkar, R., Bhattacharjee, D. (2020). A Comparative Study of Different Feature Descriptors for Video-Based Human Action Recognition. In: Mandal, J., Banerjee, S. (eds) Intelligent Computing: Image Processing Based Applications. Advances in Intelligent Systems and Computing, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-4288-6_3

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