CrimeSense: A High-Accuracy Video Crime Classification System

In this paper, a deep learning based automated crime classification system called CrimeSense is proposed. CrimeSense employs a transfer learning approach utilizing a MobileNetV2 base model fine-tuned with additional convolutional layers to extract robust features from video frames. This approach facilitates the classification of various criminal activities depicted in videos. The system achieves a remarkable accuracy of 97.8% on the UCF Crime Dataset, demonstrating its potential as a useful instrument for law enforcement and other stakeholders in video-based crime analysis. In this paper, a deep learning based automated crime classification system called CrimeSense is proposed.