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A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes

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Abstract

Deep learning base solutions for computer vision made life easier for humans. Video data contain a lot of hidden information and patterns, that can be used for Human Action Recognition (HAR). HAR can apply to many areas, such as behavior analysis, intelligent video surveillance, and robotic vision. Occlusion, viewpoint variation, and illumination are some issues that make the HAR task more difficult. Some action classes have similar actions or some overlapping parts in them. This, among many other problems, is the main reason that contributes the most to misclassification. Traditional hand-engineering and machine learning-based solutions lack the ability to handle overlapping actions. In this paper, we propose a deep learning-based spatiotemporal HAR framework for overlapping human actions in long videos. Transfer learning techniques are used for deep feature extraction. Fine-tuned pre-trained CNN models learn the spatial relationship at the frame level. An optimized Deep Autoencoder was used to squeeze high-dimensional deep features. An RNN with LSTM was used to learn the long-term temporal relationships. An iterative module added at the end to fine-tune the trained model on new videos that learns and adopt changes. Our proposed framework achieved state-of-the-art performance in spatiotemporal HAR for overlapping human actions in long visual data streams for non-stationary surveillance environments.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2019R1F1A1060668) and also the Chung-Ang University Research Grants in 2021.

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Authors and Affiliations

  1. Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock, Pakistan

    Muhammad Bilal, Muazzam Maqsood & Sadaf Yasmin

  2. Department of Electrical and Computer Engineering College of Engineering, Dhofar University, Salalah, Oman

    Najam Ul Hasan

  3. Department of Industrial Security, Chung-Ang University, Seoul, 06974, South Korea

    Seungmin Rho

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  1. Muhammad Bilal
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Correspondence to Muazzam Maqsood or Seungmin Rho.

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This paper is an extended version of our paper published in the Proceedings of the 2020 International Conference on Artificial Intelligence (ICAI), Las Vegas, USA, 27–30 July 2020.

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Bilal, M., Maqsood, M., Yasmin, S. et al. A transfer learning-based efficient spatiotemporal human action recognition framework for long and overlapping action classes. J Supercomput 78, 2873–2908 (2022). https://doi.org/10.1007/s11227-021-03957-4

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