Sensors for Object Detection, Classification and Tracking

Dear Colleagues,

In recent years, there has been a rapid and successful expansion of computer vision research in several application fields. One area that has attained great progress is object detection, the intended use of which is to determine the location and the class of all (or specific) object instances in an image, and temporal tracking of their position.

Algorithms for object detection are strictly dependent on acquisition devices (RGB cameras, thermal, infrared, multi/hyper-spectral). On the other hand, deep neural networks (DNNs) have recently emerged as a powerful machine-learning model able to learn powerful object representations/models without the need to manually design features.

The goal of this Special Issue of Sensors is to give a perspective on object detection research. It will be dedicated to highlighting both theoretical and practical aspects of object detection; deep learning-based approaches are welcomed, as well as approaches based on unconventional input sensors, such as hyperspectral or thermal images.

This Special Issue fits within the Scope of Sensors because it explores the topic of object detection from two different points of view. On one hand, new methodologies will be investigated, for example, deep learning-based approaches. On the other hand, the accent will also be on acquisition devices, and approaches able to extract information from multi- and hyperspectral images will be welcomed.

Dr. Paolo Spagnolo
Guest Editor

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