Enhanced Crime Detection in Smart Cities through Hybrid Machine Learning and Advanced Feature Extraction Techniques - Ayush Singhal (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Urban population growth has made it harder to police and monitor high-crime areas, increasing crime and insecurity. Smart cities use video surveillance for crime detection to improve security. The backlog of video data that supervisors must watch might raise mistake rates. This problem can be solved utilizing meta-heuristic optimization and Hybrid Machine Learning. This system rapidly and correctly analyzes video stream data to identify illegal behavior. This strategy should boost surveillance system efficiency and effectiveness. After pre-processing the video data using Video-to-Frame Normalization, Resizing, and Conversion, an efficient Semantic Segmentation-efficient FCN algorithm segments the frames. SIFT and the Improved Histogram of Oriented Gradients method retrieve features from segmented areas. The enhanced Relief Algorithm refines retrieved features for feature selection. Finally, a hybrid machine learning strategy for criminal anomaly detection combines transformer model, ANN, andSVM. Python is used to implement the technique.