A computer vision based framework for visual gun detection using SURF

Today's automatic visual surveillance is prime need for security and this paper presents first step in the direction of automatic visual gun detection. The objective of our paper is to develop a framework for visual gun detection for automatic surveillance. The proposed framework exploits the color based segmentation to eliminate unrelated object from an image using K-mean clustering algorithm. Speeded up robust features (SURF) interest point detector is used to locate the object (gun) in the segmented images. Our framework is robust enough in terms of scale, rotation, affine and occlusion. We have implemented and tested the system over sample images of gun, collected by us. We got promising performance of our system to detect a gun. Further, our system performs very well under different appearance of images. Thus our system is rotation, scale and shape invariant.