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ADoCW: An Automated method for Detection of Concealed Weapon

机译：Adocw：一种检测隐藏武器的自动化方法

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摘要

In technologically advanced era, surveillance is a proven method for the monitoring of the individual's activity in the crowd. Security of infrastructure, as well as individual, is one of the major concerns because of the influential growth of radical elements or suspicious persons in the society. Continuous manual monitoring of the CCTV surveillance is difficult and monotonous task, so there is an urgent requirement to develop an automated surveillance systems. The security surveillance system has potential to detect any kind of concealed object (like firearms or any weapon including knife, scissors etc.) which may pose a threat to the security. In this paper, we propose a novel framework for the detection and classification of concealed weapons through analysis of CCTV stream data. The classification framework is developed with the categorization of various concealed weapons through deep learning based object detection and classification techniques. For the detection of concealed weapon, multi-sensor stream data capturing framework is designed using sensor fusion techniques and also embedded with the feature extraction and segmentation of imsegmentation of images module. Faster R-CNN (Region-based Convolutional Neural Network) model is trained for classification ofages module. Faster R-CNN (Region-based Convolutional Neural Network) model is trained for classification of weapons over collected dataset. Finally, several directions of work and tasks are provided as future work for the various research communities.
机译：在技​​术先进的时代，监测是在人群中监测个人活动的一种行之有效的方法。基础设施的安全性，以及个人，是因为极端分子或在社会上可疑人员的影响力增长的主要关注点之一。在闭路电视监控的连续人工监测的困难和单调的任务，所以开发一个自动监控系统的迫切要求。安全监控系统有潜力来检测任何隐藏物体的（如枪支或刀具，包括任何武器，剪刀等），可能对安全构成威胁。在本文中，我们提出了检测和藏匿武器的分类，通过CCTV流数据进行分析的新框架。分类框架与各种隐藏的武器通过深基础的学习目标检测和分类方法进行分类开发。用于检测隐藏的武器的，多传感器流数据捕获框架是使用传感器融合技术设计，并且还嵌入有图像模块的imsegmentation的特征提取和分割。更快的R-CNN（基于区域的卷积神经网络）模型训练分类ofages模块。更快的R-CNN（基于区域的卷积神经网络）模型被训练用于在收集到的数据集的武器分类。最后，工作和任务的几个方向的各种研究团体提供作为未来的工作。

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