Thermal Infrared Object Tracking

8 papers with code • 0 benchmarks • 2 datasets

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Roboflow 100: A Rich, Multi-Domain Object Detection Benchmark

roboflow-ai/roboflow-100-benchmark 24 Nov 2022

The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.

217
24 Nov 2022

LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark

QiaoLiuHit/LSOTB-TIR 3 Aug 2020

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.

110
03 Aug 2020

Multi-Task Driven Feature Models for Thermal Infrared Tracking

QiaoLiuHit/MMNet 26 Nov 2019

These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.

33
26 Nov 2019

Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking

QiaoLiuHit/MLSSNet 9 Jun 2019

These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.

12
09 Jun 2019

Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns

deepneuroscience/DeepThermalImaging 6 Mar 2018

We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.

15
06 Mar 2018

PTB-TIR: A Thermal Infrared Pedestrian Tracking Benchmark

QiaoLiuHit/PTB-TIR_Evaluation_toolkit 18 Jan 2018

The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field.

66
18 Jan 2018

Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking

QiaoLiuHit/HSSNet 27 Nov 2017

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.

15
27 Nov 2017

Deep Convolutional Neural Networks for Thermal Infrared Object Tracking

QiaoLiuHit/MCFTS Knowledge-Based Systems 2017

We observe that the features from the fully-connected layer are not suitable for thermal infrared tracking due to the lack of spatial information of the target, while the features from the convolution layers are.

10
15 Oct 2017