TY - JOUR
T1 - Autonomous Selective Parts-Based Tracking
AU - Cornacchia, Maria
AU - Velipasalar, Senem
N1 - Funding Information:
Manuscript received December 13, 2018; revised April 27, 2019; accepted January 8, 2020. Date of publication January 23, 2020; date of current version February 13, 2020. The information, data, or work presented herein was funded in part by National Science Foundation (NSF) under Grant 1739748, Grant 1816732 and by the Advanced Research Projects Agency-Energy (ARPA-E), U.S. Department of Energy, under Award Number DEAR0000940. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Xiaolin Hu. (Corresponding author: Maria Cornacchia.) The authors are with the Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244 USA (e-mail: mlscalzo@syr.edu; svelipas@syr.edu). Digital Object Identifier 10.1109/TIP.2020.2967580
Publisher Copyright:
© 1992-2012 IEEE.
PY - 2020
Y1 - 2020
N2 - Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The proposed approach was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.
AB - Object tracking from videos is still a challenging task due to various changes throughout a video sequence including occlusions, motion blur, scale and other deformation changes. In this paper, we propose a selective parts-based approach, using correlation filters, that makes choices based on a consensus of the parts and global tracking. Moreover, we further enhance our parts-based approach by introducing a segmentation-assisted parts initialization. In addition, we present a genetic algorithm-based method to autonomously select various parameters of the tracking algorithm, as opposed to the common practice of manually tuning those parameters. In contrast to existing part-based methods, the proposed method does not dilute accurate tracking by averaging results over multiple parts at every frame. Instead, we take a selective approach based on the relative weight of the responses across parts. Moreover, we only make location corrections when a part diverges, and rely on these location corrections to maintain an accurate appearance model. In the case of occlusions, which are among the main reasons for using a parts-based approach, our proposed approach consistently achieves the best performance. It is due to the ability to handle occlusion and not dilute decisions with incorrect parts, that our proposed approach enables state-of-the-art performance. The proposed approach was evaluated on videos from three different challenging benchmark datasets. Our approach has resulted in better overall precision and success rates for three different base tracking approaches.
KW - Correlation filters
KW - genetic algorithm
KW - parts-based
KW - tracking
KW - video
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U2 - 10.1109/TIP.2020.2967580
DO - 10.1109/TIP.2020.2967580
M3 - Article
AN - SCOPUS:85079743352
SN - 1057-7149
VL - 29
SP - 4349
EP - 4361
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
M1 - 8967195
ER -