arrow
Return

Multi-level visual tracking with hierarchical tree structural constraint

delete2016-08-01
delete2
PRE
AI
J
Jingjing Wang
N
Nenghai Yu
F
Feng Zhu
L
Liansheng Zhuang *
DOI:10.1016/j.neucom.2016.03.010delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Recently, part-based model has drawn much attention in visual tracking for its promising results in handling occlusion and deformation. However how to divide the target into parts and how to model the relationships between parts are still open problems. In this paper, we propose a robust tracker based on multi-level target representation and hierarchical tree structural constraint. The multi-level target representation models the target at three different levels: the bounding box (top) level, the superpixel (middle) level and the keypoint (bottom) level. The relationships between parts at all levels are modeled by the proposed hierarchical tree which includes intra-layer and inter-layer structural constraints. The positions of all the parts are optimized jointly in a unified objective function taking into account both the appearance similarity and the hierarchical tree structural constraint. The appearance model and the hierarchical tree structure are updated online to adapt to the changes of the target in both appearance and structure. Extensive experiments on various challenging video sequences demonstrate that the proposed method outperforms the state-of-the-art trackers significantly. (C) 2016 Elsevier B.V. All rights reserved.
Keywords:
Object tracking
Part-based model
Multi-level representation
Hierarchical tree
Structural constraint
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Neurocomputing cover
Neurocomputing
IF:
6.5
Papers:
2.5W
Citations:
6.5W

Organization

C
chinese academy of sciences
Scholars:
56.5W
Papers: 44.9W
Citations: 704