An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications.

Object tracking is an efficient technique adopted in video surveillance applications for monitoring a particular object in a zone. This paper presents a novel efficient tracking and counting approach for Pedestrians in video sequences. For object detection, a Gaussian Mixture Model (GMM) is used to obtain binary masks. In the Speeded Up Robust Feature feature recognition, only the features of the object are retained. This significantly improves the precision of the Speeded Up Robust Feature method. For clustering the features, an enhanced grouping algorithm Density based Spatial Clustering of Application with Noise is proposed in which the motion features are grouped and the remaining features are excluded. The features are tracked based on optical flow method. For counting the number of objects, the Pedestrian eigen vectors are created based on the Speeded Up Robust Features and the eigen vectors are trained with a SVM (support vector regression machine). The proposed work combines the object detection, feature extraction, and objects counting. The experimental results validate that the proposed pedestrian tracking and counting method is efficient than the existing approaches. [ABSTRACT FROM AUTHOR]