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.