Automatic handgun detection alarm in videos using deep learning

NEUROCOMPUTING

卷:275

Article

Olmos, Roberto¹  Tabik, Siham¹  Herrera, Francisco^1,2 

[1] Univ Granada, Soft Comp & Intelligent Informat Syst Res Grp, E-18071 Granada, Spain

[2] King Abdulaziz Univ, Fac Comp & Informat Technol, Jeddah, Saudi Arabia

关键词: Classification;    Detection;    Deep learning;    Convolutional Neural Networks;    Faster R-CNN;    VGG-16;    Alarm Activation Time per Interval;   

DOI  :  10.1016/j.neucom.2017.05.012

来源: Elsevier

【 摘 要 】

Current surveillance and control systems still require human supervision and intervention. This work presents a novel automatic handgun detection system in videos appropriate for both, surveillance and control purposes. We reformulate this detection problem into the problem of minimizing false positives and solve it by i) building the key training data-set guided by the results of a deep Convolutional Neural Networks (CNN) classifier and ii) assessing the best classification model under two approaches, the sliding window approach and region proposal approach. The most promising results are obtained by Faster R-CNN based model trained on our new database. The best detector shows a high potential even in low quality youtube videos and provides satisfactory results as automatic alarm system. Among 30 scenes, it successfully activates the alarm after five successive true positives in a time interval smaller than 0.2 s, in 27 scenes. We also define a new metric, Alarm Activation Time per Interval (AATpI), to assess the performance of a detection model as an automatic detection system in videos. (c) 2017 Elsevier B.V. All rights reserved.

【 授权许可】

Free