Real-time shadow detection using multi-channel binarization and noise removal

Abstract

High-quality automatic shadow detection remains a challenging problem in image processing and computer vision. Existing techniques for shadow detection typically make use of deep learning strategies to obtain accurate shadow detection results, at the cost of demanding high processing time, making their use unsuitable for augmented reality and robotic applications. In this paper, we propose a novel approach to perform high-quality shadow detection in real time. To do so, we convert an input image into different color spaces to perform multi-channel binarization and detect different shadow regions in the image. Then, a filtering algorithm is proposed to remove the noisy false-positive shadow regions on the basis of their sizes. Experimental results evaluated in two different datasets show that the proposed approach may run entirely on the GPU, requiring only $$\approx$$ 13 ms to detect shadows in an image with $$3840 \times 2160$$ (4k) resolution. That makes our approach about 1.8 (66$$\times$$) to 4.6 (37,284$$\times$$) orders of magnitude faster than related work for 4k resolution images, at the cost of only $$\approx$$ 5% of accuracy loss compared to the best results achieved for each dataset.