Real-Time Pedestrian Tracking and Counting with TLD

Abstract EN

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.