Real-Time Pedestrian Tracking and Counting with TLD

Abstract

This paper describes a solution to solve the issue of automatic multipedestrian tracking and counting. First, background modeling algorithm is applied to actively obtain multipedestrian candidates, followed by a confirmation step with classification. Then each pedestrian patch is handled by real-time TLD (Tracking-Learning-Detection) to get a new predication position according to similarity measure. Further TLD results are compared with classification list to determine a new, disappeared, or existing pedestrian. Finally single line counting with buffer zone is employed to count pedestrians. Experiments results on the public database, PETS, demonstrate the validity of our solution.