This paper presents an adaptable method for identifying pedestrian cues. Cue detection is investigated for adults in isolation and groups. The aim is to detect a single cue for each pedestrian. Colour Inverse Maximum Likelihood Ratio (IMLR) criteria are employed to distinguish object and background regions using a mask designed to accommodate a wide range of appearances. The adaptability and specificity of the method is demonstrated using images containing trees and street furniture; structures that are often confused with pedestrians by computer vision systems. Test images of low contrast are also included to assess the sensitivity of the cue detection process. Evaluation with over 250 images gives a false positive error rate of 10% and a false negative error rate of 1.5% % under exacting detection criteria with a complexity of where n is the number of image points considered. The speed of execution is 8 mS per frame for images of 640 by 480 pixels on an Intel core i3-2310M<sup>TM</sup> CPU running at 2.10GHz with 4.00GB RAM.