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Found 8 papers, 4 papers with code
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
TCDesc: Learning Topology Consistent Descriptors for Image Matching
Our method has the following two advantages: (1) We are the first to consider neighborhood information of descriptors, while former works mainly focus on neighborhood consistency of feature points; (2) Our method can be applied in any former work of learning descriptors by triplet loss.
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
We evaluate and analyze more than 30 trackers on LSOTB-TIR to provide a series of baselines, and the results show that deep trackers achieve promising performance.
Thermal Infrared Object Tracking
Vocal Bursts Intensity Prediction
Accurate Bounding-box Regression with Distance-IoU Loss for Visual Tracking
Moreover, we introduce a classification part that is trained online and optimized with a Conjugate-Gradient-based strategy to guarantee real-time tracking speed.
Multi-Task Driven Feature Models for Thermal Infrared Tracking
These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking
These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
Region-filtering Correlation Tracking
The IRs in training samples from cyclic shifts of the base training sample severely degrade the quality of a tracking model.
Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking
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.
