FlowScan: Estimating People Flows on Sidewalks Using Dashboard Cameras Based on Deep Learning

Abstract

In this paper, we propose FlowScan: a pedestrian flow estimation technique based on a dashboard camera. Grasping flows of people is important for various purposes such as city planning and event detection. FlowScan can estimate pedestrian flows on sidewalks without taking much cost. Currently, dashboard cameras have been becoming so popular for preserving the evidence of traffic accidents and security reasons. FlowScan assumes that an application which analyzes video from the camera is installed on an on-board device. To realize such an application, we need to design a method for pedestrian recognition and occlusion-proof tracking of pedestrians. For pedestrian recognition, the application uses Deep Learning-based techniques; CNN (Convolutional Neural Networks) and LSTM (Long-Short-Term-Memory). In this process, the faces and backs of their heads are searched in the video separately to detect not only the number of pedestrians but also their directions. Then, a series of detected positions of heads are arranged into tracks depending on the similarity of locations and colors considering the knowledge about the movement of the vehicle and pedestrians. We have evaluated FlowScan using real video data recorded by a dashboard camera. The mean absolute error rate for people flow estimation of both directions was 18.5%, highlighting its effectiveness compared with the state-of-the-art.