A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration

Li, Yang; Zhu, Jianke

doi:10.1007/978-3-319-16181-5_18

Cited by 1,036 publications

(922 citation statements)

References 32 publications

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“…And several works addressed the scale estimation issues in CF-based tracking method. For example, Li et al [25] proposed a kernelized correlation translation filter with multi-resolution extension. To solve the scale estimation problem, the target in different scales are sampled first and then resized these samples into a prefixed size, and the scale with the highest correlation score is regarded as the final result.…”

Section: Scale Estimation In Dcf-based Visual Trackingmentioning

confidence: 99%

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A comparison study on the adaptive scale estimation of correlation filter-based visual tracking methods

Wang¹,

Cai²,

Wang³

et al. 2017

2017 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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Recently, discriminative correlation filter-based method becomes one of the popular directions in the field of visual tracking because of its computational efficiency and excellent performance, which make it especially suitable for realtime application. Most of them are focused only on the transition estimation. However, accurate scale estimation of the target plays a very important role in long-term tracking task and is still a challenging problem. The principle of CFbased visual tracking is introduced first. The approaches of adaptive scale estimation in correlation filter-based visual tracking methods are summarized in this paper, and their performances are analyzed by experiment comparison. The works here can provide a better understanding on the scale estimation problem for correlation filter-based visual tracking. Furthermore, maybe with the same strategy, other factors in visual tracking, such as appearance variation, can be integrated into the framework to improve the performance of correlation filter-based method.

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Section: Scale Estimation In Dcf-based Visual Trackingmentioning

confidence: 99%

“…Accordingly, to tackle the problem of the fixed template size in correlation filter tracker, Li et al [25] proposed an effective scale-adaptive scheme. Moreover, they integrate HoG and color-naming feature together to further boost the overall tracking performance.…”

Section: Multi-resolution-based Scale Estimationmentioning

confidence: 99%

A comparison study on the adaptive scale estimation of correlation filter-based visual tracking methods

Wang¹,

Cai²,

Wang³

et al. 2017

2017 IEEE International Conference on Real-Time Computing and Robotics (RCAR)

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“…The proposed tracker is an improved version of our previous method, SAMF [30]. The main difference is that NSAMF employs color probability rather than color name.…”

Section: A10 Nsamfmentioning

confidence: 99%

The Thermal Infrared Visual Object Tracking VOT-TIR2016 Challenge Results

Felsberg

Kristan

Matas

et al. 2016

Lecture Notes in Computer Science

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The Thermal Infrared Visual Object Tracking challenge 2015, VOT-TIR2015, aims at comparing short-term singleobject visual trackers that work on thermal infrared (TIR) sequences and do not apply pre-learned models of object appearance. VOT-TIR2015 is the first benchmark on shortterm tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015.

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“…In [15], Hare et al introduced structured SVM for tracking and trained a classifier using samples with structured labels. The correlation filter-based trackers [20,21,22,16,23,24] are becoming increasingly popular due to their promising performance and computational efficiency. However, most of these trackers depend on the spatiotemporal consistency of visual cues and adopt relatively risky update schemes; therefore, they can only handle short-term tracking.…”

Section: Related Workmentioning

confidence: 99%