Convolutional residual learning with sparse robust samples and multi-feature fusion for object tracking

Huiling Gao; Jie Liu; Chaorong Liu; Binshan Li; Zhengtian Zhao; Weirong Liu

doi:10.1117/12.2524414

6 May 2019 Convolutional residual learning with sparse robust samples and multi-feature fusion for object tracking

Huiling Gao, Jie Liu, Chaorong Liu, Binshan Li, Zhengtian Zhao, Weirong Liu

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110690R (2019) https://doi.org/10.1117/12.2524414
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Recently, discriminative object trackers based on deep learning have demonstrated excellent performance. However, the tracking accuracy is facing a challenge due to contaminated training samples and different complex scenarios. For this reason, we propose a tracker based on sparse robust samples and convolutional residual learning with multi-feature fusion (SR_MFCRL). First, a sparse robust sample set (SRSS) is introduced to improve robustness of the network. In this process, we first employ sparse representation to estimate the best candidate and then utilize joint detection with response peak value and occlusion detection to determine the contamination degree of the sample. Second, a multifeature fusion residual network (MRN) is proposed and its two base branches to capture response output of different features in order to achieve higher positioning accuracy. Extensive experimental results conducted on OTB-2013 illustrate that the proposed tracker achieves outstanding performance in terms of tracking accuracy and robustness.

Citation Download Citation

Huiling Gao, Jie Liu, Chaorong Liu, Binshan Li, Zhengtian Zhao, and Weirong Liu "Convolutional residual learning with sparse robust samples and multi-feature fusion for object tracking", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110690R (6 May 2019); https://doi.org/10.1117/12.2524414

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KEYWORDS

Detection and tracking algorithms

Convolutional neural networks

Image filtering

Motion models

Target detection

Feature extraction

Network architectures

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