Let’s say you’re driving on a clear day, with cars parked along both sides of the road. The view ahead is partially blocked, and suddenly, a pedestrian steps out in front of your car.
This situation, where part of the visual information (the pedestrian) is hidden by another object (the parked cars), is known as occlusion. Occlusion doesn’t just challenge human vision; it’s also a major issue for computer vision systems.
A Pedestrian Occluded in an Autonomous Driving Scenario (Source)
Computer vision, a branch of artificial intelligence (AI) that processes visual data, can struggle to detect or track objects accurately when they are occluded. This is especially true for vision tasks like object detection, which locates and identifies objects, and object tracking, which follows the objects across video frames.
For instance, occlusion can hide objects from detection and disrupt tracking continuity. This is a serious issue for applications such as autonomous driving systems, where object tracking is a crucial component. Suppose an autonomous driving system is tracking a pedestrian, and another vehicle moves in front of the pedestrian. In that case, the system may lose track of the pedestrian, and it may become a safety hazard.
In this article, we’ll explore what occlusion is in computer vision, why it matters, and the different approaches used to handle it. Let’s get started!
Occlusion is a very common issue in many computer vision applications. When objects block each other, it becomes harder for a computer vision system to detect or track them correctly.
This issue often appears in real-world environments such as busy streets or crowded places where multiple objects overlap. So, if you’re using or building a vision system, handling occlusion is key to making it more reliable. Interestingly, there are different types of occlusion based on how much of an object is hidden and what causes the blockage.
Here’s an overview of some of the key types of occlusion:
Next, let’s take a closer look at the impacts occlusion has on different computer vision tasks.
For a task like pose estimation, which involves detecting and mapping key body joints to understand posture, occluded joints or limbs can make it harder to build accurate skeletons to estimate poses. This can lower the reliability of systems like gesture control, sports analysis, or medical monitoring.
Occluded joints are a good example of how occlusion affects pose estimation. (Source)
Similarly, in action recognition, occlusion breaks the flow of motion cues. This can make activities harder to interpret in crowd or riot-monitoring systems. For instance, if part of a throwing motion is blocked, the system might mistake it for a simple wave or standing still.
Occlusion also causes issues for tasks like image classification and segmentation. Image classification can fail if parts of the object are hidden from view, making it harder for the system to recognize or label it correctly. This can cause problems for wildlife monitoring systems, where animals can be misclassified if not properly visible.
When it comes to image segmentation, where the goal is to outline an object’s exact shape, occlusion often results in incomplete or fragmented shapes because the model can’t see the full boundaries of objects. For instance, occlusion could provide wrong measurements while segmenting tumors or irregularities from medical scans.
Across all these tasks, the outcome is usually the same: occlusion reduces accuracy, increases uncertainty, and weakens performance. This can be especially risky in fields such as healthcare, surveillance, and autonomous driving.
Smaller objects are more likely to get hidden in computer vision because they can easily blend into the background or nearby items. To reduce or overcome this issue, several techniques can be used. Let’s take a look at some of them.
Traditional methods of handling occlusion are complex and computationally intensive. These approaches generally fall into two categories: 3D model-based and hardware-based methods.
The 3D model-based method relies on predefined 3D models of objects or human bodies. By matching visible parts of the object in an image or video file to the 3D model, systems can identify the shape and location of occluded parts. This approach is useful for vision tasks like pose estimation or object recognition.
On the other hand, the hardware-based approach uses visual data from multiple cameras and sensors to handle occlusion. This method uses multiple camera angles and geometric reasoning (uses geometric shapes and spatial relationships to understand an object’s structure) from different viewpoints to reduce occlusion. It can help estimate the true position of hidden objects and is used in applications like surveillance, robotics, and autonomous driving.
With advancements in technology and the growth of neural networks, computer vision systems have become better at dealing with challenges like occlusion. Deep learning techniques now help models recognize and track objects even when parts of them are hidden. Some common approaches include occlusion-aware object detectors, attention mechanisms, and occlusion-based data augmentation.
Occlusion-aware object detectors are designed to specifically handle missing or hidden parts of objects in images or video files. They often work by modeling relationships between visible and occluded regions.
Another method is using attention mechanisms. Attention mechanisms enable models to focus on the most informative parts of an image. This lets the model ignore occluded regions while still extracting meaningful features.
In addition to this, using occlusion-based data augmentation takes things further. Data augmentation is usually used to diversify a dataset and help the AI model learn patterns during training. Augmenting the data with occlusion simulation can help train AI models to recognize partially hidden objects. This is done by artificially occluding some parts of the training images.
When you augment data, you can factor in occlusion. (Source)
Researchers are working on better ways to help computer vision systems deal with occlusion. Two effective techniques are multi-modal fusion and temporal context analysis.
Multi-modal fusion combines data from different sensors and devices, such as LiDAR and depth sensors. Bringing together information from multiple sources gives the system a more complete view of the scene, helping it detect objects that might be hidden or partly blocked.
Meanwhile, temporal context analysis looks at motion and continuity in videos. By analyzing how objects move over time, the system can predict where an occluded object is likely to appear, making tracking smoother and more accurate.
Now that we have a better understanding of what occlusion is and how to handle it, let’s take a closer look at real-world use cases where it’s vital.
In the streets of San Francisco, Waymo robotaxis driving themselves are a common sight. Can you imagine what would happen if the Waymo robotaxi didn’t see someone crossing the street?
Whether it’s a pedestrian, another car, or even an animal crossing the road, not being able to detect them can result in serious safety concerns. Methods to handle occlusion in such situations often involve techniques that we have already discussed, such as using attention mechanisms and LiDAR.
For instance, a recently published research paper explored how 3D object detection can be used to handle occlusion in autonomous driving. It works by combining LiDAR, which captures precise spatial shapes, with cameras, which provide detailed visual information.
An Example of 3D Object Detection Using LiDAR and Cameras. (Source)
Behind the scenes, an attention mechanism is used to focus on the most important information and decide which sensor’s data is more reliable in each situation. The combined data is then used to create a top-down, or bird’s-eye-view, map that makes it possible for the system to predict accurate 3D positions of objects.
In particular, the attention mechanism plays a pivotal role in handling occlusion. When the camera’s view is blocked or parts of an object are hidden, the system relies more on LiDAR’s 3D data and less on the camera’s visual input.
Under clear conditions, it does the opposite, giving more weight to the camera’s detailed visuals. This balanced use of LiDAR and camera data makes the system much better at detecting objects affected by occlusion than traditional methods.
A pear hidden between leaves might easily be mistaken for an apple. We can usually tell the difference from a distance, but can an agricultural robot do the same? Occlusion can make that difficult for computer vision–based robots.
When fruits are partially hidden, a robot might misidentify them or miss them altogether. This makes it harder for the robot to perform key tasks like locating, recognizing, and picking fruits for harvesting. Missing fruits reduces efficiency and lowers the overall productivity of farms, especially in dense orchards where trees and branches often overlap.
Researchers have developed an active deep sensing strategy to help harvesting robots handle occlusion. In this approach, the robot adjusts its viewpoint or sensor settings to get a clearer view of the fruit. Instead of staying fixed in one position, it identifies areas of interest and moves to angles that provide the best visibility.
By repositioning itself and focusing on better perspectives, the robot can see around leaves, branches, and other obstacles. This active sensing approach improves its ability to detect and recognize fruits, even in cluttered environments.
Harvesting robots can capture images from multiple angles to overcome occlusion. (Source)
Facial recognition is becoming a common innovation at airports and other public spaces. However, if these systems aren’t properly tuned, even something as simple as scratching your nose could cause them to identify you as someone else. Such errors are serious and can lead to issues like mistaken identity or false matches.
AI security systems like facial recognition rely heavily on computer vision. In large, crowded areas such as airports, occlusion often becomes a challenge. When people are partially or fully blocked from view, the system may fail to detect or correctly identify them. This can result in false alarms, inaccurate tracking, and reduced reliability in security operations. To minimize these problems, researchers have developed systems that are better at handling occlusion.
One such model is PedHunter. It is designed to detect pedestrians in crowded scenes, where people are often occluded.
PedHunter uses a mask-guided module, which helps the system focus only on the visible parts of each person instead of the areas that are blocked. It does this by creating a mask, a kind of outline, that highlights what’s visible in the image. By concentrating on these visible regions, the system can identify people more accurately even in dense or cluttered environments.
PedHunter also applies strict classification rules to reduce false detections in crowded spaces. During training, it uses occlusion-simulated data augmentation, which exposes the model to examples of partially hidden people so it can better handle similar situations in the real world.
The Pedhunter Model Architecture. (Source)
Something to keep in mind when exploring ways to handle occlusion in computer vision is that every method comes with its own challenges. Here are some of the key limitations to be aware of:
To overcome these challenges, it helps to have the right support. At Objectways, we focus on high-quality data labeling and annotation, creating diverse and accurately annotated datasets that strengthen computer vision models. Our expertise ensures your AI systems can handle occlusion, adapt across domains, and perform reliably in real-world conditions.
Generative AI is emerging as a game-changer for handling occlusion in computer vision. You might wonder how a technology known for creating AI-generated images can help with this problem.
In fact, generative AI can reconstruct the hidden parts of objects that are blocked from view. Techniques like diffusion-based image inpainting can fill in the missing areas, allowing detectors and trackers to work with a more complete picture.
A good example of this is the use of Generative Adversarial Networks (GANs) for occlusion removal. This approach works in two steps: first, it detects the occluded regions (for instance, a fence blocking part of an image), and then it uses a GAN model to recreate the hidden sections.
The model learns both the structure and texture of the scene, ensuring that the reconstructed parts look natural and realistic. Tests on datasets such as Places2, CelebA, and the IITKGP_Fence dataset show that this method already outperforms many existing techniques for removing complex or irregular occlusions.
First, the model masks the occlusion (the fence), then uses GANs to reconstruct the hidden parts. (Source)
Occlusion is still one of the most puzzling problems in computer vision. It makes it harder for computer vision systems to detect, track, and recognize objects in applications such as self-driving cars, robots, and security systems.
Traditional methods and modern deep learning have improved occlusion handling, but real-world environments still pose significant hurdles. However, promising future solutions, such as generative AI for reconstructing hidden parts and combining data from different sensors, are redefining occlusion handling.
You can also partner with a solution provider like Objectways, which can handle all the heavy lifting for you. With precise, diverse, and high-quality annotated datasets from Objectways, AI models can be trained to better handle occlusion, scale across domains, and deliver reliable performance in dynamic, real-world applications.
Book a call with our experts to see how we can support your computer vision journey.
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