Predicting Pedestrian Counts in Crowded Scenes With Rich and High-Dimensional Features

Estimating the number of pedestrians in surveillance images and videos has important applications in intelligent transportation systems. This problem is particularly challenging when the scenes are densely crowded, in which the techniques of tracking a single pedestrian has limited effectiveness. Alternative approaches employ statistical learning algorithms to infer pedestrian counts directly from visual features computed on images or scenes. In this paper, we describe a system for predicting pedestrian counts that significantly extends the utility of those ideas. Our approach incorporates a richer set of features for statistical modeling. While these features give rise to regression problems in a high-dimensional space, we leverage learning techniques to reduce dimensionality while still attaining high accuracy for predicting the number of pedestrians. Empirical results have validated our strategy. Specifically, our system outperforms state-of-the-art methods on standard benchmark tasks by a large margin.