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Detection of individual activities in video sequences based on fast interference discovery and semi-supervised method

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Abstract

Auto understanding of human activities in video is an increasing necessity in some application realms. The existing methods for human’s activity identification are divided into two methods: activity recognition and activity detection. The most important challenge in activity detection realm is activity boundary false detection which decreases system accuracy. In this research, an activity detection system was suggested denoting rapid interference and sewing it. Although it has improved accuracy it has also accuracy time, activities in suggested system were replayed more usefully and influenced by creating a descriptor denoting movable and apparent form. The suggested system was tested on Weizmann dataset and reached an accuracy of 93.34%. Furthermore, the proposed system in activity recognition was tested on KTH dataset and reached an accuracy of 93.63%. When activity recognition is stated as a learning case, sufficient labeled educational examples must be used. But labeling the video data is expensive, so the useful method uses unlabeled and labeled examples, during the learning process, this idea is the basic foundation of the semi-supervised method. In this research, a semi-supervised method with co-training algorithm appearance and active learning was suggested which improved the efficiency of semi-supervised learning that was tested.

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Notes

  1. Geometric Properties of Motion

  2. Detection based on the Probability Changes

  3. Support Vector Machine

  4. Dynamic Co_Training

  5. Imperialism Competetive Algorithm with using Information Exchange and Simulated Annealing

  6. Bag Of Words

  7. Dynamic Frame Warping

  8. Discrete Cosine Transform

  9. Principal Component Analysis

  10. Linear Discriminant Analysis

  11. Vector of Locally Aggregated Descriptors

  12. True Positive

  13. False Positive

  14. Histogram of Optical Flow

  15. Histogram Oriented Gradient

  16. Motion Boundary Histogram

  17. Imperialism Competitive Algorithm

  18. Particle Swarm Optimization

  19. Genetic Algorithm

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Authors and Affiliations

  1. Department of Computer Engineering, Alzahra University, Tehran, Iran

    Mohammad Reza Keyvanpour

  2. Department of Computer Engineering, Data Mining Laboratory, Alzahra University, Tehran, Iran

    Neda Khanbani & Zahra Aliniya

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  1. Mohammad Reza Keyvanpour
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  2. Neda Khanbani
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  3. Zahra Aliniya
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Correspondence to Mohammad Reza Keyvanpour.

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Keyvanpour, M.R., Khanbani, N. & Aliniya, Z. Detection of individual activities in video sequences based on fast interference discovery and semi-supervised method. Multimed Tools Appl 80, 13879–13910 (2021). https://doi.org/10.1007/s11042-020-10418-2

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