Shadow detection and removal is important to deal with traffic image sequences. Cast shadow of vehicle may lead to an inaccurate object feature extraction and erroneous scene analysis. Furthermore, separate vehicles can be connected through shadow. Both can confuse object recognition systems. In this paper, a robust method is proposed for detecting and removing active cast shadow in monocular color image sequences. Background subtraction method is used to extract moving blobs in color and gradient dimensions, and the YCrCb color space is adopted for detecting and removing the cast shadow. Even when shadows link different vehicles, it can detect the each vehicle figure using modified mask by shadow bar. Experimental results from town scenes show that proposed method is effective and the classification accuracy is sufficient for general vehicle type classification.

This paper proposes a method for estimating the number and locations of multiple targets distributed in the ocean. This is achieved by calculating the cross spectral density matrix (CSDM) generated from individual sound sources and applying them to a minimum variance distortionless response (MVDR), a nonlinear processor. The individual CSDMs are calculated by separating and extracting a Sequence CLEAN-based data vector from the CSDM of the data vector received from multiple targets. Numerical simulations demonstrate that the proposed method improves the MVDR performance in the case of multiple targets.