Abstract
In the last few decades, crowd detection has gained much interest from the research community to assist a variety of applications in surveillance systems. While human detection in partially crowded scenarios have achieved many reliable works, a highly dense crowd-like situation still is far from being solved. Densely crowded scenes offer patterns that could be used to tackle these challenges. This problem is challenging due to the crowd volume, occlusions, clutter and distortion. Crowd region classification is a precursor to several types of applications. In this paper, we propose a novel approach for crowd region detection in outdoor densely crowded scenarios based on color variation context and RGB channel dissimilarity. Experimental results are presented to demonstrate the effectiveness of the new color-based features for better crowd region detection.