Hybrid approach using map-based estimation and class-specific Hough forest for pedestrian counting and detection

Abstract

The system proposed in this study deals with pedestrian counting and detection in intelligent video surveillance systems. It is a hybrid of map-based and detection-based approaches, and combines the advantages of both. After the foreground objects being segmented, the map-based module, which implicitly compensates the perspective distortion by integrally projecting the features onto a given direction, is triggered to estimate the number of pedestrians in each foreground region. Then, a class-specific Hough forest is employed to locate individuals. Experimental results have validated our strategy. The proposed map-based module has the ability of accurately estimating the count for each region. Also, the estimation can speed up the process of locating individuals by providing cues like the number of targets and the approximate size of each target. The proposed detection-based module not only locates pedestrians, but deals with enhancing the accuracy of the counting as well.