Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning.

Highlights • A labeled database for cold steel detection. • Selection of the best model for cold steel weapon detection. • A new brightness guided preprocessing procedure, called Darkening and Contrast at Learning and Test (DaCoLT). • A real time cold steel detection system for surveillance videos. 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. [ABSTRACT FROM AUTHOR]