Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos

Abstract

Estimating the number of pedestrians based upon surveillance videos and images has been a critical task while implementing intelligent signal controls at intersections. However, this has been a difficult task considering the pedestrian waiting area is an outdoor scenario with complex and time-varying surrounding environment. In this study, a method for estimating pedestrian counts based on multisource video data has been proposed. First, the partial least squares regression (PLSR) model is developed to estimate the number of pedestrians from single-source video (either visible light video or infrared video). Meanwhile, the temporal feature of the scenario (daytime or nighttime) is identified based on visible light video. According to the recognized time periods, pedestrian count detection results from the visible light and infrared video data can be obtained with preset corresponding confidence levels. The empirical experiments showed that this fusion method based on environment perception holds the benefits of 24-hour monitoring for outdoor scenarios at the pedestrian waiting area and substantially improved accuracy of pedestrian counting.