Computer vision system for working time estimation by human activities detection in video frames

The goal of the research is to develop and to test methods for detecting people, parametric points for their hands and their current working tools in the video frames. The following algorithms are implemented: humans bounding boxes coordinates detection in video frames; human pose estimation: parametric points detection for each person in video frames; detection of the bounding boxes coordinates of the defined tools in video frames; estimation of which instrument the person is using at the certain moment. To implement algorithms, the existing computer vision models are used for the following tasks: Object detection, Pose estimation, Object overlaying. Machine learning system for working time detection based on computer vision is developed and deployed as a web-service. Recall, precision and f1-score are used as a metric for multi-classification problem. This problem is defined as what type of tool the person uses in a certain frame of video (Object Overlaying). Problem solution for action detection for the railway industry is new in terms of work activity estimation from video and working time optimization (based on human action detection). As the videos are recorded with a certain positioning of cameras and a certain light, the system has some limitations on how video should be filmed. Another limitation is the number of working tools (pliers, wrench, hammer, chisel). Further developments of the work might be connected with the algorithms for 3D modeling, modeling the activity as a sequence of frames (RNN, LSTM models), Action Detection model development, time optimization for the working process, recommendation system for working process from video activity detection.