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Advances in Object and Activity Detection in Remote Sensing Imagery

Objektkategorie:
Elektronische Ressource
Person/Institution:
Ulhaq, Anwaar
Gomes, Douglas Pinto Sampaio
Verlag:
MDPI - Multidisciplinary Digital Publishing Institute
Veröffentlichungsort:
Basel
Entstehungszeit:
2022
Umfang, Illustration, Format:
1 Online-Ressource (170 p.)
Sprache:
Nicht zu entscheiden
Bereitstellende Institution:
Forschungsbibliothek Gotha
GND Explorer
Abstract:
The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms
Objekttext:
English

Zugriff und Nutzungsmöglichkeiten

Universität Erfurt
Forschungsbibliothek Gotha
Schloss Friedenstein
Schlossplatz 1
99867 Gotha
+49 361 737-5540
bibliothek.gotha(at)uni-erfurt.de
Datensatz angelegt am:
2023-04-12
Zuletzt geändert am:
2023-01-29
In Portal übernommen am:
2023-04-12