Thermal Infrared Object Tracking
8 papers with code • 0 benchmarks • 2 datasets
These leaderboards are used to track progress in Thermal Infrared Object Tracking
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.
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.
The ability to evaluate the TIR pedestrian tracker fairly, on a benchmark dataset, is significant for the development of this field.
Deep Thermal Imaging: Proximate Material Type Recognition in the Wild through Deep Learning of Spatial Surface Temperature Patterns
We evaluated the performance of the system by training it to recognise 32 material types in both indoor and outdoor environments.
These two similarities complement each other and hence enhance the discriminative capacity of the network for handling distractors.
These two feature models are learned using a multi-task matching framework and are jointly optimized on the TIR tracking task.
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.
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.