Moving Cast Shadow Detection in Video Based on New Chromatic Criteria and Statistical Modeling

Abstract

A novel moving cast shadow detection method is presented in this paper to detect and remove the cast shadows from the foreground. First, the foreground is detected using the global foreground modeling (GFM) method. Second, the moving cast shadow is detected and removed from the foreground using a new moving cast shadow detection method that contains four hierarchical steps. In the first step, a set of new chromatic criteria is presented to detect the candidate shadow pixels in the HSV color space. In the second step, a new shadow region detection method is proposed to cluster the candidate shadow pixels into shadow regions. In the third step, a statistical shadow model, which uses a single Gaussian distribution to model the shadow class, is presented to classify shadow pixels. In the last step, an aggregated shadow detection method is presented for final shadow detection. Experiments using the public video data 'Highway-3' and the real traffic data from the New Jersey Department of Transportation (NJDOT) show the feasibility of the proposed method.