Human Activity Recognition in car workshop

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 problems 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 extract 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 with compared to the fastDTW with 86%. The second experiment was conducted to measure the performance of one-dollar algorithm with different participants. The results show that 1 dollar recognizer achieved an accuracy of 95% when tested on 10 different videos.