Detecting knives in surveillance videos are very urgent for public safety. In general, the research in identifying dangerous
weapons is relatively new. Knife detection is a very challenging task because knives vary in size and shape. Besides, it easily refects lights that reduce the visibility of knives in a video sequence. The refection of light on the surface of the knife
and the brightness on its surface makes the detection process extremely difcult, even impossible. This paper presents an
adaptive technique for brightness enhancement of knife detection in surveillance systems. This technique overcomes the
brightness problem that faces the steel weapons and improves the knife detection process. It suggests an automatic threshold
to assess the level of frame brightness. Depending on this threshold, the proposed technique determines if the frame needs
to enhance its brightness or not. Experimental results verify the efciency of the proposed technique in detecting knives
using the deep transfer learning approach. Moreover, the most four famous models of deep convolutional neural networks
are tested to select the best in detecting knives. Finally, a comparison is made with the-state-of-the-art techniques, and the
proposed technique proved its superiority. |
