Abstract: Human activity recognition has become so widespread in recent times. Due to the modern advancements of technology, it has become an important solution to many prob-lems in various fields such as medicine, industry, and sports. And this subject got the attention of a lot of researchers. Along with problems like wasted time in maintenance centers, we proposed a system that extracts worker poses from videos by using pose classification. In this paper, we have tested two algorithms to detect worker activity. This system aims to detect and classify positive and negative worker’s activities in car maintenance centers such as (changing the tire, changing oil, using the phone, standing without work). We have conducted two experiments, the first experiment was for comparison between algorithms to determine the most accurate algorithm in recognizing the activities performed. The experiment was done using two different algorithms (1 dollar recognizer and Fast Dynamic time warping) on 3 participants in a controlled area. The one-dollar recognizer has achieved a 97% accuracy compared to the fastDTW with 86%. The second experiment was conducted to measure the performance of a one-dollar algorithm with different participants. The results show that a 1 dollar recognizer achieved an accuracy of 94.2% when tested on 420 different videos.

Omar Magdy and Ayman Atia, “Human Activity Recognition in Car Workshop” International Journal of Advanced Computer Science and Applications(IJACSA), 13(4), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130495

@article{Magdy2022,
title = {Human Activity Recognition in Car Workshop},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130495},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130495},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {4},
author = {Omar Magdy and Ayman Atia}
}