Siamese infrared and visible light fusion network for RGB-T tracking

Fusion of Infrared and Visible-Light Images

Hierarchical Siamese Network for Thermal Infrared Object Tracking

Most thermal infrared (TIR) tracking methods are discriminative, which treat 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-...

Fusion of Thermal Infrared and Visible Images Based on Multi-scale Transform and Sparse Representation

Due to the differences between the visible and thermal infrared images, combination of these two types of images is essential for better understanding the characteristics of targets and the environment. Thermal infrared images have most importance to distinguish targets from the background based on the radiation differences, which work well in all-weather and day/night conditions also in land s...

A Twofold Siamese Network for Real-Time Object Tracking

Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similaritylearning Siamese network. An important design choice in SA-Siam...

Similarity Mapping with Enhanced Siamese Network for Multi-Object Tracking

Multi-object tracking has recently become an important area of computer vision, especially for Advanced Driver Assistance Systems (ADAS). Despite growing attention, achieving high performance tracking is still challenging, with state-of-theart systems resulting in high complexity with a large number of hyper parameters. In this paper, we focus on reducing overall system complexity and the numbe...