Adaptive spatially regularized correlation filter tracking via level set segmentation

Huang, Guopeng; Ji, Hongbing; Zhang, Wenbo

doi:10.1117/1.jei.28.6.063013

Cited by 2 publications

(1 citation statement)

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“…The Staple [25] tracker uses the target's color-statistics information to build a histogram-score model and combine it with the model that is learned on the spatial layout of target, showing robust and balanced performance in many complex environments. The adaptive spatially-regularized correlation filter (ASRCF) [26] tracker designed a novel model which could optimize the spatial regularization weight and the filter coefficients at the same time.…”

Section: Correlation-filter-based Trackersmentioning

confidence: 99%

HKSiamFC: Visual-Tracking Framework Using Prior Information Provided by Staple and Kalman Filter

Xing

2020

Sensors

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In the field of visual tracking, trackers based on a convolutional neural network (CNN) have had significant achievements. The fully-convolutional Siamese (SiamFC) tracker is a typical representation of these CNN trackers and has attracted much attention. It models visual tracking as a similarity-learning problem. However, experiments showed that SiamFC was not so robust in some complex environments. This may be because the tracker lacked enough prior information about the target. Inspired by the key idea of a Staple tracker and Kalman filter, we constructed two more models to help compensate for SiamFC’s disadvantages. One model contained the target’s prior color information, and the other the target’s prior trajectory information. With these two models, we design a novel and robust tracking framework on the basis of SiamFC. We call it Histogram–Kalman SiamFC (HKSiamFC). We also evaluated HKSiamFC tracker’s performance on dataset of the online object tracking benchmark (OTB) and Temple Color (TC128), and it showed quite competitive performance when compared with the baseline tracker and several other state-of-the-art trackers.

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Section: Correlation-filter-based Trackersmentioning

confidence: 99%