An improvement for the foreground recognition method using shadow removal technique for indoor environments

In this paper, we present an efficient and simple shadow detection algorithm for indoor environments, as well as give a brief description on the advantages of this method. In this method, we use three types of approaches: image enhancement, chromaticity consistency, and gradient features. Multiple shadow direction is becoming an increasingly challenging task for many moving shadow detection algorithms because some objects have large self-shadows. Our system is able to achieve good performance solving spread shadow problems in indoor scenes, leading to improved foreground segmentation in surveillance scenarios. The image enhancement approach is first employed to input images to generate high-quality images for artificial light source indoor areas. Afterwards, the chromaticity information is utilized to create a mask of possible candidate shadow pixels. Subsequently, gradient features are applied to remove foreground pixels that have been incorrectly included in the mask. In comparison with existing algorithms, the proposed method can correctly detect and remove shadow pixels to identify original foreground shapes without distortion for delivering object recognition and tracking tasks.