Deep Feature Based Siamese Network for Visual Object Tracking

Su-Chang Lim, Jun-Ho Huh () and Jong-Chan Kim ()
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Su-Chang Lim: Department of Computer Engineering, Sunchon National University, Suncheon 57992, Korea
Jun-Ho Huh: Department of Data Science, (National) Korea Maritime and Ocean University, Busan 49112, Korea
Jong-Chan Kim: Department of Computer Engineering, Sunchon National University, Suncheon 57992, Korea

Energies, 2022, vol. 15, issue 17, 1-21

Abstract: One of the most important and challenging research subjects in computer vision is visual object tracking. The information obtained from the first frame consists of limited and insufficient information to represent an object. If prior information about robust representation that can represent an object well is not sufficient, object tracking fails when not robustly responding to changes in features of the target object according to various factors, namely shape, illumination variation, and scene distortion. In this paper, a real-time single object tracking algorithm is proposed based on a Siamese network to solve this problem. For the object feature extraction, we designed a fully convolutional neural network that removes a fully connected layer and configured a convolution block consisting of a bottleneck structure that preserves the information in a previous layer. This network was designed as a Siamese network, while a regional proposal network was combined at the end of the network for object tracking. The ImageNet Large-Scale Visual Recognition Challenge 2017 dataset was used to train the network in the pre-training phase. Then, in the experimental phase, the object tracking benchmark dataset was used to quantitatively evaluate the network. The experimental results revealed that the proposed tracking algorithm produced more competitive results compared to other tracking algorithms.

Keywords: object tracking; convolution neural network; AI; siamese network; image similarity; CUDA; Python; PyTorch; computer vision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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