1. A computer implemented method for detecting an object in a scene comprising the steps of: receiving, using a processor, classified threat patches having a depth map of 3D pixels of an image in the scene using a processor, wherein each said pixel includes associated 3D position data; projecting, using said processor, said classified threat patches into a pre-generated vertical support histogram to facilitate selection of said projected threat patches having a score value within a sufficiency criterion, said vertical support histogram is a 2-D footprint of 3-D pixels in the image of the threat patches and the vertical support histogram is generated by retrieving and summing up height value of each of the 3-D pixel that satisfies depth and height constraints, according to: 
 N max row ( Z )= X res ×f x /Zx 
 N max col ( Z )= X res ×f x /Zx 
 N ( Z )= N max row ( Z )× N max col ( Z ) where N max row is maximum number of pixels in each image row, Z is distance of a rectangular grid cell from a camera, X res is a lateral value, f x is a horizontal focal length, N max col is maximum number of image rows in the height band [H max , H min ], f y is a vertical focal length, H max is the maximum height visible in the image at the distance Z and H min is the minimum height visible in the image at the distance Z; grouping, using said processor, said selected patches in a plane fit to obtain a region of interest (ROI) in the image, said ROI comprising a plurality of grid cells with 2D pixels; and processing, using said processor, said region of interest in the image to detect said object.