Human crowd analysis has common utilizations from the urban engineering and traffic management to law enforcement. They all need a crowd for first being detected, and is the issue that has been dealt with in the present study. Considering an image, the algorithm that has been proposed in this paper performs a segmentation of that image to crowd and non-crowd areas. The fundamental concept is capturing two main characteristics of the crowd: (a) on a narrower scale, its main elements have to appear like humans (only weakly so, as a result of the low resolution, dressing variations, occlusion, and so on), whereas (b) on the wider scale, the crowd intrinsically includes elements of the redundant appearance. The proposed approach makes use of that through the utilization of underlying statistical framework which has been based on the quantized features of the SURF. The two previously mentioned characteristics of the crowds have been obtained through the resultant statistical model responses' feature vector, which describe the level of crowd-like appearances around the location of an image with the increase of the spatial level around it.