# Occlusion Handling in Generic Object Detection: A Review

arXiv: https://arxiv.org/abs/2101.08845 · HTML: https://mlsanity.com/paper/2101.08845/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 38 of its 75 references that are in the map.
- First posted on arXiv 21 Jan 2021.

## Builds on (most cited first, 30 of 38)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Context Encoders: Feature Learning by Inpainting](https://mlsanity.com/paper/1604.07379/) (2016)
- [Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data](https://mlsanity.com/paper/1908.04616/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- [Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection](https://mlsanity.com/paper/1708.01642/) (2017)
- [Semantic Amodal Segmentation](https://mlsanity.com/paper/1509.01329/) (2015)
- [Self-Supervised Scene De-occlusion](https://mlsanity.com/paper/2004.02788/) (2020)
- [Amodal Instance Segmentation](https://mlsanity.com/paper/1604.08202/) (2016)
- [Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation](https://mlsanity.com/paper/1804.08864/) (2018)
- [SeGAN: Segmenting and Generating the Invisible](https://mlsanity.com/paper/1703.10239/) (2017)
- [Occlusion-aware R-CNN: Detecting Pedestrians in a Crowd](https://mlsanity.com/paper/1807.08407/) (2018)
- [Robust Object Detection under Occlusion with Context-Aware CompositionalNets](https://mlsanity.com/paper/2005.11643/) (2020)
- [Synthesizing Training Data for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1702.07836/) (2017)
- [Compositional Convolutional Neural Networks: A Deep Architecture with Innate Robustness to Partial Occlusion](https://mlsanity.com/paper/2003.04490/) (2020)
- [A-Fast-RCNN: Hard Positive Generation via Adversary for Object Detection](https://mlsanity.com/paper/1704.03414/) (2017)
- [A survey of face recognition techniques under occlusion](https://mlsanity.com/paper/2006.11366/) (2020)
- [Compositional Convolutional Neural Networks: A Robust and Interpretable Model for Object Recognition under Occlusion](https://mlsanity.com/paper/2006.15538/) (2020)
- [Combining Compositional Models and Deep Networks For Robust Object Classification under Occlusion](https://mlsanity.com/paper/1905.11826/) (2019)
- [MVTec D2S: Densely Segmented Supermarket Dataset](https://mlsanity.com/paper/1804.08292/) (2018)
- [DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection](https://mlsanity.com/paper/1412.5661/) (2014)
- [Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models](https://mlsanity.com/paper/1905.04598/) (2019)

## Built on by (most cited first, 25 of 25)

- [Behind the Curtain: Learning Occluded Shapes for 3D Object Detection](https://mlsanity.com/paper/2112.02205/) (2021)
- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [A survey of synthetic data augmentation methods in computer vision](https://mlsanity.com/paper/2403.10075/) (2024)
- [A Tri-Layer Plugin to Improve Occluded Detection](https://mlsanity.com/paper/2210.10046/) (2022)
- [Image Amodal Completion: A Survey](https://mlsanity.com/paper/2207.02062/) (2022)
- [NOMAD: A Natural, Occluded, Multi-scale Aerial Dataset, for Emergency Response Scenarios](https://mlsanity.com/paper/2309.09518/) (2023)
- [Point2Graph: An End-to-end Point Cloud-based 3D Open-Vocabulary Scene Graph for Robot Navigation](https://mlsanity.com/paper/2409.10350/) (2024)
- [The Impact of Partial Occlusion on Pedestrian Detectability](https://mlsanity.com/paper/2205.04812/) (2022)
- [ICON2: Reliably Benchmarking Predictive Inequity in Object Detection](https://mlsanity.com/paper/2306.04482/) (2023)
- [Efficient Biological Data Acquisition through Inference Set Design](https://mlsanity.com/paper/2410.19631/) (2024)
- [TOTNet: Occlusion-Aware Temporal Tracking for Robust Ball Detection in Sports Videos](https://mlsanity.com/paper/2508.09650/) (2025)
- [BBBD: Bounding Box Based Detector for Occlusion Detection and Order Recovery](https://mlsanity.com/paper/2204.12841/) (2022)
- [Latent Enhancing AutoEncoder for Occluded Image Classification](https://mlsanity.com/paper/2402.06936/) (2024)
- [BBoxCut: A Targeted Data Augmentation Technique for Enhancing Wheat Head Detection Under Occlusions](https://mlsanity.com/paper/2503.24032/) (2025)
- [SynSpill: Improved Industrial Spill Detection With Synthetic Data](https://mlsanity.com/paper/2508.10171/) (2025)
- [GPT-4 for Occlusion Order Recovery](https://mlsanity.com/paper/2509.22383/) (2025)
- [Investigating the Robustness and Properties of Detection Transformers (DETR) Toward Difficult Images](https://mlsanity.com/paper/2310.08772/) (2023)
- [Mask Guided Gated Convolution for Amodal Content Completion](https://mlsanity.com/paper/2407.15203/) (2024)
- [Improving Apple Object Detection with Occlusion-Enhanced Distillation](https://mlsanity.com/paper/2409.01573/) (2024)
- [OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions](https://mlsanity.com/paper/2411.15729/) (2024)
- [Psych-Occlusion: Using Visual Psychophysics for Aerial Detection of Occluded Persons during Search and Rescue](https://mlsanity.com/paper/2412.05553/) (2024)
- [Break Out the Silverware -- Semantic Understanding of Stored Household Items](https://mlsanity.com/paper/2512.23739/) (2025)
- [Tiny Collaborative Inference for Occlusion-Robust Object Detection](https://mlsanity.com/paper/2606.02894/) (2026)
- [Evolution of Accuracy and Visual-Cognitive Errors in a Decade of Vision-Language AI Models](https://mlsanity.com/paper/2607.09654/) (2026)
- [RealOOB: A Definition-Consistent Real-World Oriented Occlusion Boundary Benchmark](https://mlsanity.com/paper/2608.30820/) (2026)

## Abstract (arXiv)

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.