Vision-Based Counting of Pedestrians and Cyclists

Abstract

This paper describes a vision-based cyclist and pedestrian counting method. It presents a data collection prototype system, as well as pedestrian and cyclist detection, tracking, and counting methodology. The prototype was used to collect approximately 50 hours of data which have been used for training and testing. Counting is done using a cascaded classifier. The first stage of the cascade detects the pedestrians or cyclists, whereas the second stage discriminates between these two classes. The system is based on a state-of-the-art pedestrian detector from the literature, which was augmented to explore the geometry and constraints of the target application. Namely, foreground detection, geometry prior information, and temporal moving direction (optical flow) are used as inputs to a multi-cue clustering algorithm. In this way, false alarms of the detector are reduced and better fitted detection windows are obtained. The presented project was the result of a partnership with the City of Pittsburgh with the objective of providing actionable data for government officials and advocates that promote bicycling and walking.