Occlusion Handling in Generic Object Detection: A Review

Abstract

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.

To be published in the proceedings of IEEE 19th World Symposium on Applied Machine Intelligence and Informatics

References in corpus (14)

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Cited by in corpus (5)

• A survey of synthetic data augmentation methods in computer vision
• NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios
• SynSpill: Improved Industrial Spill Detection With Synthetic Data
• Mask Guided Gated Convolution for Amodal Content Completion
• BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery