Crowd Density Analysis and tracking

Crowd Density Analysis (CDA) aims to compute concentration of crowd in surveillance videos. This paper core is to estimate the crowd concentrations using crowd feature tracking with optical flow. Local features are extracted using Features for Accelerated Segment Test (FAST) algorithm per frame. Optical flow tracks the features between frames of the surveillance video. This process identifies the crowd features in consecutive frames. Kernel density estimator computes the crowed density in each successive frame. Finally individual people are tracked using estimated flows. The drawback of this method is similar to suffered by most of the estimation methods in this class that is reliability. Hence testing with three popular optical flow models is initiated to find the best optical flow. Three methods are Horn-Schunck (HSOF), Lukas-Kanade (LKOF) and Correlation optical flow (COF). Five features extraction methods were tested along with the three optical flow methods. FAST features with horn-schunck estimates crowed density better than the remaining methods. People tracking application with this algorithm gives good tracks compared to other methods.