Case Study of Task-Execution Pattern Analysis Based on Object Arrangement

Abstract

Deep-learning-based computer vision technologies have been commoditized owing to the synergy among various software infrastructures that enhances experimentʼs reproducibility. As applications of such a "commoditized" object detection method, this paper introduces two case studies, spatial and temporal analyses of task-execution pattern through an observation of object arrangement on a working table. In the spatial analysis, we extracted concept models given by each worker as an area segmentation on the flat workspace surface and role-assignment to each segmented area. This was done by an approach to treat the detected objects as if they were words in a document. In the temporal analysis, we found some specific patterns that relates to workerʼs strategy in cooking, by visualizing time series variation in the number of detected objects in a specific area on the working table.