Position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion

Ruijie Cao; Xina Cheng; Yanchao Liu; Takeshi Ikenaga

doi:10.1117/12.2669979

24 February 2023 Position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion

Ruijie Cao, Xina Cheng, Yanchao Liu, Takeshi Ikenaga

Author Affiliations +

Proceedings Volume 12590, Third International Conference on Computer Vision and Information Technology (CVIT 2022); 125900B (2023) https://doi.org/10.1117/12.2669979
Event: 2022 3rd International Conference on Computer Vision and Information Technology (CVIT 2022), 2022, Beijing, China

Abstract

Object tracking plays an important role in the computer vision field and has many applications such as video surveillance and vehicle navigation. But the occlusion problem is one of the most challenging problems in the applications. Although there are many approaches in the object tracking field that focus on dealing with occlusion scenes, the occlusion with large size barriers and long occlusion time still cannot be solved. To handle the problems, this paper proposes a reliable tracking method based on particle filter focus on long-term full occlusion with large size barriers. In this paper the large size is defined as pixel width from 350 to 600 in fixed resolution images. and the long term is defined as occlusion frame number from 180 to 600. First, this paper proposed a particle position reset module to replace the resampling process during the occlusion periods to solve the problem of losing the target after occlusion. In addition, a hybrid feature based likelihood model is proposed for the occlusion happening and ending judgments. Experiments on the extreme occlusion situation sequences demonstrate the reliability and accuracy of the proposed work on these challenging scenes. The algorithm finally implements the average 92% success rate at the tested sequences.

Citation Download Citation

Ruijie Cao, Xina Cheng, Yanchao Liu, and Takeshi Ikenaga "Position reset and hybrid feature based particle filter tracking for large-size and long-term full occlusion", Proc. SPIE 12590, Third International Conference on Computer Vision and Information Technology (CVIT 2022), 125900B (24 February 2023); https://doi.org/10.1117/12.2669979

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available