Extended social force model-based mean shift for pedestrian tracking under obstacle avoidance
Research output: Contribution to journal › Article
extended social force model-based mean shift tracking algorithm, in which the pedestrian's environment is taken into full consideration. First, in order to show how the environment impacts pedestrian movements from the viewpoint of force, an extended social force model is presented by considering the interaction between target and obstacle. Furthermore, according to characteristics of pedestrian tracking, directional weights and speed weights are introduced to adjust the strength of the force concerning the difference of individual perspectives and relative velocities. Finally, the initial target position is predicted by Newton's laws of motion and then the mean shift method is integrated to track the target position. Experiment results showed that this algorithm achieved an encouraging performance when an obstacle occurred. An object that moves fast or changes its moving directions quickly can also be robustly tracked in real time by using the proposed algorithm.
|Journal||IET Computer Vision|
|Early online date||21 Sep 2016|
|State||Published - Feb 2017|
- Extended Social Force_CV16
Rights statement: This paper is a preprint of a paper accepted by IET Computer Vision and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at IET Digital Library.
Accepted author manuscript (Post-print), 1 MB, PDF-document