Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
Xin Li, Qiao Liu, Nana Fan, Zhenyu He, Hongzhi Wang

TL;DR
This paper introduces HSSNet, a hierarchical spatial-aware Siamese CNN for thermal infrared object tracking, which improves discrimination by coupling similarity verification with spatial and semantic feature extraction.
Contribution
The paper presents a novel hierarchical Siamese network architecture with spatial awareness for TIR tracking, trained on visible data and transferred to TIR domain, enhancing tracking accuracy.
Findings
Achieves superior performance on VOT-TIR 2015 and 2016 benchmarks.
Outperforms existing state-of-the-art TIR tracking methods.
Demonstrates effective transfer learning from visible to thermal infrared data.
Abstract
Most thermal infrared (TIR) tracking methods are discriminative, treating the tracking problem as a classification task. However, the objective of the classifier (label prediction) is not coupled to the objective of the tracker (location estimation). The classification task focuses on the between-class difference of the arbitrary objects, while the tracking task mainly deals with the within-class difference of the same objects. In this paper, we cast the TIR tracking problem as a similarity verification task, which is coupled well to the objective of the tracking task. We propose a TIR tracker via a Hierarchical Spatial-aware Siamese Convolutional Neural Network (CNN), named HSSNet. To obtain both spatial and semantic features of the TIR object, we design a Siamese CNN that coalesces the multiple hierarchical convolutional layers. Then, we propose a spatial-aware network to enhance the…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Advanced Chemical Sensor Technologies · Infrared Target Detection Methodologies
MethodsSiamese Network
