In this ever connected society, CCTVs have had a pivotal role in enforcing safety and security of the citizens by recording unlawful activities for the authorities to take actions. In a smart city context, using Deep Convolutional Neural Networks (DCNN) to detection violence and weaponized violence from CCTV videos will provide an additional layer of security by ensuring real-time detection around the clock. In this work, we introduced a new specialised dataset by gathering real CCTV footage of both weaponized and non-weaponized violence as well as non-violence videos from YouTube. We also proposed a novel approach in merging consecutive video frames into a single salient image which will then be the input to the DCNN. Results from multiple DCNN architectures have proven the effectiveness of our method by having the highest accuracy of 99\%. We also take into consideration the efficiency of our methods through several parameter trade-offs to ensure smart city sustainability.
翻译:在这个联系不断的社会里,闭路电视通过记录非法活动,让当局采取行动,在加强公民安全保障方面发挥了关键作用;在智能城市中,利用深革命神经网络(DCNN)从闭路电视视频中探测暴力和武器化暴力,通过确保全天24小时实时检测,将提供额外的一层安全;在这项工作中,我们通过从YouTube中收集武器化和非武器化暴力的真正闭路电视视频以及非暴力视频,推出了一个新的专门数据集;我们还提议了一种新颖的办法,将连续视频框架合并成一个单一的突出图像,然后成为DCNNN的投入;多个DCNN网络结构的结果通过达到99-_____的最高精确度,证明了我们的方法的有效性;我们还通过若干参数交换,考虑到我们的方法的效率,以确保城市的智能可持续性。