Occlusion Handling in Generic Object Detection: A Review

Occlusion Handling in Generic Object Detection: A Review

The significant power of deep learning networks has led to enormous development in object detection. Over the last few years, object detector frameworks have achieved tremendous success in both accuracy and efficiency. However, their ability is far from that of human beings due to several factors, o...

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Veröffentlicht in:arXiv.org 2021-01
Hauptverfasser: Saleh, Kaziwa, Szénási, Sándor, Vámossy, Zoltán
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Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Object recognition
Occlusion
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Vámossy, Zoltán
description The significant power of deep learning networks has led to enormous development in object detection. Over the last few years, object detector frameworks have achieved tremendous success in both accuracy and efficiency. However, their ability is far from that of human beings due to several factors, occlusion being one of them. Since occlusion can happen in various locations, scale, and ratio, it is very difficult to handle. In this paper, we address the challenges in occlusion handling in generic object detection in both outdoor and indoor scenes, then we refer to the recent works that have been carried out to overcome these challenges. Finally, we discuss some possible future directions of research.
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subjects Computer Science - Artificial Intelligence
Computer Science - Computer Vision and Pattern Recognition
Object recognition
Occlusion
title Occlusion Handling in Generic Object Detection: A Review
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