Real-time crowd behavior detection using SIFT feature extraction technique in video sequences
Shivali Choudhary, Nitish Kumar Ojha, Vrijendra Singh · 2017
These days Crowd behavior detection in video surveillance is a latest research area in the field of computer vision. It focuses on the demanding assignment of monitoring crowded events for outbreaks of violent behavior. Such scenes have a need of human assessor to monitor multiple video screens, presenting crowds of people in a frequently changing sea of activity. In this paper, we propose an innovative approach for real-time crowd behavior detection using SIFT feature extraction technique in Video Sequences. For any detection and classification the feature extraction and feature optimization is very important metrics. So in proposed work SIFT feature extraction technique are used in appropriate segmented for background subtraction in video sense. After that feature extraction is applied in all regions, but a suitable feature extraction is not possible. To overcome this problem we have used Genetic Algorithm to optimize the extracted feature set. A genetic algorithm is best optimization technique and also operates in large data set. At last performance metrics of proposed work is calculates. In which we can compared propose work with previous existing work. And we calculate the performance metrics like precision rate, recall rate, and accuracy. The real-time crowd behavior using SIFT feature extraction technique in Video Sequences is implemented using Image Processing Toolbox within Matlab Software.