Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/220049
Title: Visual Tracking via Joint Discriminative Appearance Learning
Authors: Chong Sun;Fu Li;Huchuan Lu;Gang Hua
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a discriminative tracking method based on dictionary learning and support vector machine (SVM) classification, where the dictionary and the classifier are jointly learned within a unified objective function. A discriminative differential tracking method is proposed, which estimates the motion parameters iteratively by the gradient-based method to maximize the SVM classification score, leading the bounding box to move purposively. As the target appearance may change across frames, an online update scheme is exploited, which not only reserves the discriminative information, but also adaptively accounts for the appearance changes in the dynamic scenes. We examine the proposed method on the benchmark challenging image sequences, including heavy occlusion, pose change, illumination variation, and so on. Extensive evaluations demonstrate that the proposed tracker performs favorably against other state-of-the-art algorithms.
URI: http://localhost/handle/Hannan/220049
volume: 27
issue: 12
More Information: 2567,
2577
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7523900.pdf2.96 MBAdobe PDF
Title: Visual Tracking via Joint Discriminative Appearance Learning
Authors: Chong Sun;Fu Li;Huchuan Lu;Gang Hua
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a discriminative tracking method based on dictionary learning and support vector machine (SVM) classification, where the dictionary and the classifier are jointly learned within a unified objective function. A discriminative differential tracking method is proposed, which estimates the motion parameters iteratively by the gradient-based method to maximize the SVM classification score, leading the bounding box to move purposively. As the target appearance may change across frames, an online update scheme is exploited, which not only reserves the discriminative information, but also adaptively accounts for the appearance changes in the dynamic scenes. We examine the proposed method on the benchmark challenging image sequences, including heavy occlusion, pose change, illumination variation, and so on. Extensive evaluations demonstrate that the proposed tracker performs favorably against other state-of-the-art algorithms.
URI: http://localhost/handle/Hannan/220049
volume: 27
issue: 12
More Information: 2567,
2577
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7523900.pdf2.96 MBAdobe PDF
Title: Visual Tracking via Joint Discriminative Appearance Learning
Authors: Chong Sun;Fu Li;Huchuan Lu;Gang Hua
Year: 2017
Publisher: IEEE
Abstract: In this paper, we present a discriminative tracking method based on dictionary learning and support vector machine (SVM) classification, where the dictionary and the classifier are jointly learned within a unified objective function. A discriminative differential tracking method is proposed, which estimates the motion parameters iteratively by the gradient-based method to maximize the SVM classification score, leading the bounding box to move purposively. As the target appearance may change across frames, an online update scheme is exploited, which not only reserves the discriminative information, but also adaptively accounts for the appearance changes in the dynamic scenes. We examine the proposed method on the benchmark challenging image sequences, including heavy occlusion, pose change, illumination variation, and so on. Extensive evaluations demonstrate that the proposed tracker performs favorably against other state-of-the-art algorithms.
URI: http://localhost/handle/Hannan/220049
volume: 27
issue: 12
More Information: 2567,
2577
Appears in Collections:2017

Files in This Item:
File SizeFormat 
7523900.pdf2.96 MBAdobe PDF