Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature

Sensors
Yuankun LiJie Guo

Abstract

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.

References

Dec 22, 2010·IEEE Transactions on Pattern Analysis and Machine Intelligence·B BabenkoSerge Belongie
Dec 14, 2011·IEEE Transactions on Pattern Analysis and Machine Intelligence·Z KalalJiri Matas
Apr 2, 2014·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society·Wei ZhongMing-Hsuan Yang
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Yi WuMing-Hsuan Yang
Sep 10, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·João F HenriquesJorge Batista
Dec 25, 2015·IEEE Transactions on Pattern Analysis and Machine Intelligence·Sam HarePhilip H S Torr
Feb 19, 2016·IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society· Kaihua Zhang Ming-Hsuan Yang

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MOSSE
STRUCT
KCF
VGG
ACMD
MEEM
SRDCFad
SAMF
GAUSSIAN

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