Intelligent Traffic Violation Detection

Precise feature extraction is vital in traffic scene analysis, especially in vehicle detection and segmentation. In this research, the performance of Histogram of Oriented Gradients (HOG) in extracting meaningful visual features from traffic images is assessed. Two instances are under examination: one with an empty road and the other with vehicles on the road. By comparing and analyzing HOG features of both scenarios, we see how traffic affects feature intricacy. The features are then processed further with Decision Tree, Support Vector Classifier (SVC), Multi-Layer Perceptron (MLP), Random Forest and Logistic Regression in order to analyze their contribution towards object detection. The findings reveal that under notraffic conditions, HOG is seen to target static background structures most of the time, while under vehicle conditions, the detected features are more complex with the presence of added edges and contours. This work emphasizes the effect of traffic on feature extraction and validates the application of HOG-based methods for automated traffic surveillance and vehicle detection.