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2022
Conference Paper
Titel

Class-aware Object Counting

Abstract
Estimating the correct number of objects in a given natural scene is a common challenge in computer vision. Natural scenes usually contain multiple object categories and varying object densities. Detection-based algorithms are well suited for class-aware object counting and low object counts. However, they underperform with high or varying numbers of objects. To address this challenge, we propose an end-to-end approach to enhance an existing detection based method with a multi-class density estimation branch. The results of both branches are fed into a successive count estimation network, which estimates object counts for each category. Although these numbers do not contain any 10-calization information, they can be used as a valuable indicator for verifying the exactness of the object detector results and improving its counting performance. In order to demonstrate the effectiveness, we evaluate our method on common object detection datasets.
Author(s)
Michel, Andreas
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Groß, Wolfgang
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Schenkel, Fabian
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Middelmann, Wolfgang
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB
Hauptwerk
IEEE Winter Conference on Applications of Computer Vision Workshops, WACVW 2022. Proceedings
Konferenz
Winter Conference on Applications of Computer Vision (WACV) 2022
Real-World Surveillance - Applications and Challenges Workshop (RWS) 2022
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DOI
10.1109/wacvw54805.2022.00053
Externer Link
Externer Link
Language
English
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