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.