Journal Article10.1016/J.KNOSYS.2019.05.032
Multi-pattern correlation tracking
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TL;DR: By taking advantage of multiple filters to model different appearance patterns, the proposed MPCT tracker can not only capture dynamic appearance changes under complex scenes but also deal with severe occlusion and model drift problems to achieve better tracking performance.
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Abstract: In this paper, we propose a novel multi-pattern correlation tracker (MPCT) which deeply models the appearance of the target object for robust tracking. Specifically, multiple correlation filters are learned to capture different appearance patterns of the target object during the tracking process and each filter represents one specific appearance pattern. With the proposed reliable and matching score, a two stage selection algorithm is developed to select a suitable correlation filter to localize the target object. To effectively obtain different filters, we design an online evaluation algorithm to generate filters for different appearance patterns. By taking advantage of multiple filters to model different appearance patterns, the proposed MPCT tracker can not only capture dynamic appearance changes under complex scenes but also deal with severe occlusion and model drift problems to achieve better tracking performance. Extensive experimental results prove that the proposed tracking algorithm performs superiorly against several state-of-the-art tracking methods on challenging tracking benchmarks.
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References
High-Speed Tracking with Kernelized Correlation Filters
TL;DR: A new kernelized correlation filter is derived, that unlike other kernel algorithms has the exact same complexity as its linear counterpart, which is called dual correlation filter (DCF), which outperform top-ranking trackers such as Struck or TLD on a 50 videos benchmark, despite being implemented in a few lines of code.
Fully-Convolutional Siamese Networks for Object Tracking
Luca Bertinetto,Jack Valmadre,João F. Henriques,Andrea Vedaldi,Philip H. S. Torr +4 more
- 08 Oct 2016
TL;DR: A basic tracking algorithm is equipped with a novel fully-convolutional Siamese network trained end-to-end on the ILSVRC15 dataset for object detection in video and achieves state-of-the-art performance in multiple benchmarks.
Online Object Tracking: A Benchmark
Yi Wu,Jongwoo Lim,Ming-Hsuan Yang +2 more
- 23 Jun 2013
TL;DR: Large scale experiments are carried out with various evaluation criteria to identify effective approaches for robust tracking and provide potential future research directions in this field.
Visual object tracking using adaptive correlation filters
David S. Bolme,J. Ross Beveridge,Bruce A. Draper,Yui Man Lui +3 more
- 13 Jun 2010
TL;DR: A new type of correlation filter is presented, a Minimum Output Sum of Squared Error (MOSSE) filter, which produces stable correlation filters when initialized using a single frame, which enables the tracker to pause and resume where it left off when the object reappears.
Object Tracking Benchmark
TL;DR: An extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria is carried out to identify effective approaches for robust tracking and provide potential future research directions in this field.