Unified Graph-Based Multicue Feature Fusion for Robust Visual Tracking

Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this paper. To this...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on cybernetics 2020-06, Vol.50 (6), p.2357-2368
Main Authors: Walia, Gurjit Singh, Ahuja, Himanshu, Kumar, Ashish, Bansal, Nipun, Sharma, Kapil
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Adaptive appearance model
Clutter
Data analysis
Encoding
Feature extraction
feature fusion
Iterative methods
Object tracking
Occlusion
Optical tracking
outlier detection
Outliers (statistics)
Qualitative analysis
Reliability
Robustness
Target tracking
visual tracking
Visualization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Visual tracking is a complex problem due to unconstrained appearance variations and a dynamic environment. The extraction of complementary information from the object environment via multiple features and adaption to the target's appearance variations are the key problems of this paper. To this end, we propose a robust object tracking framework based on the unified graph fusion (UGF) of multicue to adapt to the object's appearance. The proposed cross-diffusion of sparse and dense features not only suppresses the individual feature deficiencies but also extracts the complementary information from multicue. This iterative process builds robust unified features which are invariant to object deformations, fast motion, and occlusion. Robustness of the unified feature also enables the random forest classifier to precisely distinguish the foreground from the background, adding resilience to background clutter. In addition, we present a novel kernel-based adaptation strategy using outlier detection and a transductive reliability metric. The adaptation strategy updates the appearance model to accommodate variations in scale, illumination, and rotation. Both qualitative and quantitative analyses on benchmark video sequences from OTB-50, OTB-100, VOT2017/18, and UAV123 show that the proposed UGF tracker performs favorably against 18 other state-of-the-art trackers under various object tracking challenges.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2019.2920289