communities_Crime

A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

犯罪是指蓄意实施的、可造成人身或心理伤害、财产损毁或损失的行为，且可依据犯罪的严重程度，由国家或其他主管机构予以惩处。犯罪活动的数量与形式正以惊人的速度增长，迫使各执法机构研发高效手段以采取预防措施。在犯罪率快速攀升的当下，传统破案手段因节奏缓慢、效率低下而难以取得预期成效。因此，若能研发出可提前对犯罪行为进行详细预测的方案，或是开发出能够协助警务人员工作的“智能工具”，将大幅减轻警方负担并助力犯罪预防工作。为实现这一目标，我们建议引入机器学习（Machine Learning, ML）与计算机视觉（Computer Vision）算法及相关技术。本文阐述了此类方法在部分案例中的应用成果，这些案例也激励我们在该领域开展进一步研究。犯罪侦查与预防模式实现转变的核心动因，在于采用此类技术的主管机构在应用前后的统计观测结果。本研究的唯一目的，在于探究执法机构或主管部门应如何结合机器学习与计算机视觉技术，以更高的准确率与更快的速度开展犯罪侦查、预防与侦破工作。总而言之，机器学习与计算机视觉技术将为执法机构带来革命性变革。