论文标题
通过使用3D卷积神经网络，对预防犯罪的入店行窃案件的可疑行为检测
Suspicious Behavior Detection on Shoplifting Cases for Crime Prevention by Using 3D Convolutional Neural Networks
论文作者
论文摘要
犯罪会造成人类和经济造成的巨大损失。每年，由于袭击，犯罪和骗局，数十亿美元损失。监视摄像机网络正在生成大量数据，监视人员无法实时处理所有信息。人类的视线有其局限性，在处理监视时，视觉焦点是最关键的焦点之一。可能在不同的屏幕细分市场或不同的监视器上发生犯罪，工作人员可能不会注意到。我们的提案通过分析普通人将视为典型条件但可能导致犯罪的特殊情况来关注入店行窃犯罪。尽管其他方法可以识别犯罪本身，但我们取而代之的是模拟可疑行为（可能在人犯罪之前发生的行为），通过检测录像的精确细分市场，很可能包含入店行窃的犯罪。通过这样做，我们为员工提供了更多的行动和预防犯罪机会。我们实现了一个3DCNN模型作为视频提取器，并在由日常操作和入店行窃样品组成的数据集上测试了其性能。结果令人鼓舞，因为它正确地识别了即将发生犯罪的案件的75％。
Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks are generating vast amounts of data, and the surveillance staff can not process all the information in real-time. The human sight has its limitations, where the visual focus is among the most critical ones when dealing with surveillance. A crime can occur in a different screen segment or on a distinct monitor, and the staff may not notice it. Our proposal focuses on shoplifting crimes by analyzing special situations that an average person will consider as typical conditions, but may lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior -- the one that may occur before a person commits a crime -- by detecting precise segments of a video with a high probability to contain a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily-action and shoplifting samples. The results are encouraging since it correctly identifies 75% of the cases where a crime is about to happen.
