Action detection in public spaces by video analysis

Abstract

Assessing the value of public spaces is an issue in urban planning. People's staying time in the target space is used as one of the indexes to quantify the value of public space. Until now, surveys have been conducted by people visually checking videos, but labor is required and work efficiency is required. Therefore, in this study, we examined a method to automatically evaluate the staying time by utilizing RTFM (Robust Temporal Feature Magnitude learning) shown by Tian et al. RTFM is a model for detecting abnormal action of surveillance cameras, and this method was applied to detect specific human behavior in public spaces. In this paper, we conducted RTFM learning on images taken with a handy camera in a public space, and compared the accuracy when the loss function and activation function were changed.