Shadow Detection Based on Combination of HSV Color Space and Principal Component Analysis in Surveillance Videos

In common background subtraction method usually the shadow of objects is extracted as the moving objects that cause some errors in the performance of Intelligent Transportation Systems (ITS). In this paper, an effective algorithm based on combination of HSV color space and Principal Component Analysis is proposed. In this algorithm, the candidate shadow region is detected by using HSV color space. In this step, some part of vehicles may be detected as the moving shadow. So, to compensate and improve the performance of moving shadow detection, principal components analysis algorithm is applied to recognize the automobile object by modeling the automobile based on orthogonal eigen vectors of database. Our proposed algorithm is evaluated on real and operational videos of ITS. The obtained results demonstrate the efficiently and effectiveness of our proposed algorithm in the ITS applications.