Image Processing

IMU-Blur commenced our evaluation by randomly selecting 8350 clear images (aka. backgrounds) from existing image datasets~\cite{zhou2017places,quattoni2009recognizing}. By capturing IMU data during the motion blur induced by the RealSense D455i camera, we synthesized a dataset of 8350 blurred images accompanied by corresponding blur heat maps. Ultimately, this dataset, namely IMU-Blur, contains 6680 triplets for training and 1670 triplets for testing.

PSP-MP, a subway platform passenger standing position dataset created using Blender software. The dataset includes 200 test scenarios. The IMG directory stores binocular images of the subway platform layer, with a single image resolution of 1280 * 720Pixel and a JPG format. The GT directory stores information such as the standing position, height, and orientation of passengers on the platform layer in the platform coordinate system.

This dataset is from "One-Stage Cascade Refinement Networks for Infrared Small Target Detection." It includes 427 infrared images and 480 targets (due to the lack of infrared sequences, SIRST also contains infrared images at a wavelength of 950 nm, in addition to shortwave and midwave infrared images). Approximately 90% of the images contain only one target, while about 10% have multiple targets (which may be overlooked in sparse/significant methods due to global unique assumptions).

Human pose estimation has applications in numerous fields, including action recognition, human-robot interaction, motion capture, augmented reality, sports analytics, and healthcare. Many datasets and deep learning models are available for human pose estimation within the visible domain. However, challenges such as poor lighting and privacy issues persist. These challenges can be addressed using thermal cameras; nonetheless, only a few annotated thermal human pose datasets are available for training deep learning-based human pose estimation models.