The objective of this workshop is to highlight cutting edge advances and state-of-the-art work being made in the exponentially growing field of PBVS (previously “Object Tracking & Classification Beyond the Visible Spectrum” - OTCBVS) integrating sensor processing, algorithms, and applications. PBVS involves deep theoretical research in sub-areas of image processing, machine vision, pattern recognition, machine learning, robotics, and augmented reality within and beyond the visible spectrum. Advancing vision-based systems includes frameworks and methods featured in PBVS.
The computer vision community has typically focused mostly on the development of vision algorithms for object detection, tracking, and classification with visible range sensors in day and office-like environments. In the last decade, infrared (IR), depth, thermal and other non-visible imaging sensors were used only in special area like medicine and defense. The relatively lower interest level in those sensory areas in comparison to computer vision was due in part to their high cost, low resolutions, poor image quality, lack of widely available data sets, and/or lack of consideration of the potential advantages of the non-visible part of the spectrum. These objections are becoming overcome as sensory technology is advancing rapidly and the sensor cost is dropping dramatically. Image sensing devices with high dynamic range and IR sensitivity have started to appear in a growing number of applications ranging from defense and automotive domains to home and office security.
We encourage the submission of original papers that cover the topics of interest mentioned below. In order to develop robust and accurate vision-based systems that operate in and beyond the visible spectrum, not only existing methods and algorithms originally developed for the visible range should be improved and adapted, but also entirely new algorithms that consider the potential advantages of nonvisible ranges are certainly required. The fusion of visible and non-visible ranges, like radar and IR images, depth images or IMU information, or thermal and visible spectrum images as well as acoustic images, is another dimension to explore for higher performance of vision-based systems.
The 19th IEEE CVPR Workshop on Perception Beyond the Visible Spectrum (PBVS’2023) fosters connections between communities in the machine vision world ranging from public research institutes to private, defense, and federal laboratories. PBVS brings together academic pioneers, industrial and defense researchers and engineers in the field of computer vision, image analysis, pattern recognition, machine learning, signal processing, artificial intelligence, sensor exploitation, and HCI.
PBVS’2023 challenges are: Thermal Image Super-Resolution Challenge (TISR’2023), Multi-modal Aerial View Object Classification Challenge (MAVOC'2023), and Semi-Supervised Hyperspectral Object Detection Challenge (SSHODC'2023). For more information about the challenges, the datasets, the evaluation approaches and measures as well as the deadline for participation, please visit the challenge webpage.