Abstract
The automatic detection of cold steel weapons handled by one or multiple persons in surveillance videos can help reducing crimes. However, the detection of these metallic objects in videos faces an important problem: their surface reflectance under medium to high illumination conditions blurs their shapes in the image and hence makes their detection impossible. The objective of this work is two-fold: (i) To develop an automatic cold steel weapon detection model for video surveillance using Convolutional Neural Networks(CNN) and (ii) strengthen its robustness to light conditions by proposing a brightness guided preprocessing procedure called DaCoLT (Darkening and Contrast at Learning and Test stages). The obtained detection model provides excellent results as cold steel weapon detector and as automatic alarm system in video surveillance. (C) 2018 Elsevier B.V. All rights reserved.