A novel approach for multi-cue feature fusion for robust object tracking

Object tracking is a significant problem of computer vision due to challenging environmental variations. Single cue appearance model is not sufficient to handle the variations. To this end, we propose a multi-cue tracking framework in which complementary cues namely, LBP and HOG were exploited to de...

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Bibliographic Details
Published in:Applied intelligence (Dordrecht, Netherlands) Netherlands), 2020-10, Vol.50 (10), p.3201-3218
Main Authors: Kumar, Ashish, Walia, Gurjit Singh, Sharma, Kapil
Format: Article
Language:English
Subjects:
Artificial Intelligence
Changing environments
Computer Science
Computer vision
Datasets
Feature extraction
Machines
Manufacturing
Mechanical Engineering
Occlusion
Processes
Qualitative analysis
Robustness
Tracking
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Summary:Object tracking is a significant problem of computer vision due to challenging environmental variations. Single cue appearance model is not sufficient to handle the variations. To this end, we propose a multi-cue tracking framework in which complementary cues namely, LBP and HOG were exploited to develop a robust appearance model. The proposed feature fusion captures the high-level relationship between the features and diminishes the low-level relationship. Transductive reliability is also integrated at each frame to make tracker adaptive with the changing environment. In addition, K-Means based classifier creates clear and concise boundary between positive and negative fragments which are further used to update the reference dictionary. This adaptation strategy prevents the erroneous updation of the proposed tracker during background clutters, occlusion, and fast motion. Qualitative and quantitative analysis on challenging video sequences from OTB-100 dataset, VOT dataset and UAV123 reveal that the proposed tracker performs favorably against 13 others state-of-the-art trackers.
ISSN:0924-669X
1573-7497
DOI:10.1007/s10489-020-01649-9