Even with all its advancement in technology, computer vision system cannot competes with nature’s gift—the brains, that arranges the objects quickly and extract the necessary information from huge data. A bio-inspired feature selection method is proposed for detecting the humans using saliency detection. It is performed by tuning prominent features such as color, orientation, and intensity in bottom-up approach to locate the probable candidate regions of humans in an image. Further, the results improved in detection phase that makes use of weights learned from training samples to ignore non-human regions in the candidate regions. The overall system has an accuracy rate of 90 % for detecting the human region.
R. Aarthi, Amudha, J., and Priya, U., “A Generic Bio-inspired Framework for Detecting Humans Based on Saliency Detection”, in Artificial Intelligence and Evolutionary Algorithms in Engineering Systems: Proceedings of ICAEES 2014, Volume 2, P. L Suresh, Dash, S. Sekhar, and Panigrahi, B. Ketan, Eds. New Delhi: Springer India, 2015, pp. 655–663.