Video Analysis for Crowd and Traffic Management

Abstract

The Government of India develops the road and transport facilities to reduce traffic congestion in mass gatherings at rush time. Based on the public needs, the video scene detection system provides a video classification technique to analyze the video sequences from various crowd and traffic congestion excellently. This classification technique is most significant in many applications such as video surveillance, traffic control analysis and crowd monitoring. A number of applications for detecting and classifying videos have been proposed in the literature. Scene analysis in the video is a big challenge in many Crowd monitoring and Traffic controlling system. The proposed system presents an automatic video scene detection and analysis method for detecting and classifying crowd and traffic video scenes. Histogram Oriented Gradients (HOG) feature descriptor is extracted features from the video scenes. K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers are used to classify the video scenes. The video scenes are collected from various crowd videos of Tamil Nadu. The experimental results show that KNN with HOG features performs well with 97% accuracy.