Occlusion Handling in Generic Object Detection: A Review
It addresses the problem of occlusion handling in object detection for researchers and practitioners, but it is incremental as it reviews existing works without presenting new results.
This paper reviews the challenges of occlusion in generic object detection, highlighting its difficulty due to varying locations, scales, and ratios, and discusses recent works and future directions to address these issues.
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.