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When your favorite basketball player takes a shot, you can often tell whether it’ll go in the hoop or miss. As a human, you’re able to predict this because you’ve probably seen various basketball games and know what to expect. Now imagine a machine watching a basketball game through a camera, trying to figure out if the shot will go in the hoop.
It’s a lot more complicated. Replicating the visual intelligence we develop through experience isn’t as simple as it sounds. In fact, ball trajectory prediction is a fascinating area of research that explores how machines can analyze motion, spin, and angle to predict where a ball will land.
As early as 1537, Italian mathematician Niccolò Tartaglia discovered that a cannonball travels farthest when fired at a 45-degree angle. His findings built the foundation for how we understand motion and prediction today.
Now, with advances in computer science and artificial intelligence (AI), machines can predict a ball’s trajectory accurately and in real time. By analyzing details like the ball’s position, speed, spin, and angle, AI systems can forecast where it will land. This helps with applications ranging from player training and performance analysis to healthcare and robotics.
In particular, computer vision is a field of AI that helps machines interpret and understand visual information from the world around them. It’s what enables systems to recognize objects, track their movement, and make sense of what they see through cameras or video.
When applied to ball trajectory prediction, computer vision models such as Ultralytics YOLO11 and Ultralytics YOLO26 can help a system detect and track the ball frame by frame. This visual data provides accurate information about the ball’s position in each frame, which can then be used to estimate its speed and direction over time. AI models and various algorithms can then use this data to predict where the ball will go next.
In this article, we’ll explore ball trajectory prediction, the various approaches involved, and the role of computer vision. Let’s get started!
The importance of ball trajectory prediction
Beyond being an interesting area of research, ball trajectory prediction is driving the development of practical and impactful technology across many fields. For instance, in autonomous vehicles, trajectory prediction enables cars to anticipate the future paths of other vehicles, cyclists, and pedestrians. By forecasting where these moving objects are likely to be in the next few seconds, self-driving systems can make safer and more efficient decisions by adjusting speed, changing lanes, or avoiding potential collisions in advance.
Similarly, in robotics, ball trajectory prediction makes it possible for machines to detect, track, and react to moving objects with high precision. This is especially useful in automated systems that need to handle fast-moving items in real time, such as industrial robots or delivery drones.
An overview of how ball trajectory prediction works
So, how does predicting a ball’s trajectory actually work? It depends on the method being used. In general, every approach aims to do the same thing: understand how the ball moves now and forecast where it will go next.
Here’s a glimpse of some of the different approaches used:
Physics-based modeling: This approach uses the laws of motion and aerodynamics to calculate how factors such as velocity, launch angle, gravity, and air resistance affect the ball’s flight path.
Machine learning models: These models analyze large sets of motion data to learn patterns in how a ball moves, allowing them to make accurate predictions without relying on explicit physical equations.
Computer vision-based prediction: This method uses video footage to detect and track the ball frame by frame, producing visual data that helps algorithms estimate its speed, direction, and future position.
Sensor-based systems: These systems use data from motion sensors, radar, or infrared cameras to precisely measure the ball’s position, velocity, and spin in real time.
Hybrid methods: They combine two or more techniques. For instance, integrating computer vision with physics-based modeling to achieve more accurate and reliable trajectory predictions.
Next, we’ll take a closer look at how computer vision-based ball trajectory prediction works and the algorithms that make it possible.
The basics of ball trajectory prediction
Before we look at how computer vision plays a role in ball trajectory prediction and how these systems work, let’s break down the basics.
Ball trajectory prediction generally involves two main parts: tracking and motion modeling. Tracking algorithms are used to follow the ball frame by frame in a video, estimating where it will appear next. These methods are useful for short-term, real-time predictions.
On the other hand, motion modeling methods (typically involving physics or machine learning) go a step further. They aim to predict the ball’s complete path, including where it will land.
Simply put, tracking determines the ball’s current position and motion, while modeling uses that information to estimate its future path and landing point. Together, they enable accurate, real-time ball trajectory prediction.
A closer look at tracking and motion modeling
You might be wondering why the distinction between tracking and prediction matters. As we explore this topic further, you’ll notice that many tracking algorithms don’t just track. They also make short-term predictions about where the ball will appear next.
For example, computer vision models like YOLO11, which support object tracking through tracking algorithms such as ByteTrack or SORT, use detection data from each video frame to estimate the ball’s next position in real time. This helps the system maintain accurate tracking even when the ball moves quickly or becomes partially hidden.
However, while these models are great at following motion, they typically predict only the ball’s next position rather than its entire flight path or landing point. That’s where motion modeling methods become essential.
In other words, a computer vision model by itself can’t fully predict a ball’s trajectory. It can detect and track the ball’s position frame by frame, but it doesn’t account for the physical forces that influence its motion, such as gravity, spin, drag, or air resistance.
Fig 2. An example of ball detection and segmentation using YOLO11. (Source)
To make accurate long-term predictions, the visual data from computer vision is often combined with motion modeling methods. These methods use either physics-based equations or machine learning algorithms to analyze the ball’s motion over time and forecast its complete path, including where it will land.
Common algorithms used for ball tracking in computer vision
Tracking algorithms are a crucial part of ball trajectory prediction systems. They estimate the ball’s position and motion over time while handling challenges like brief occlusion (when the ball is temporarily hidden from view by another object.
Next, we’ll walk through some common tracking algorithms.
Kalman Filter
The Kalman Filter is a mathematical algorithm used to estimate an object’s position and velocity over time, even when measurements are noisy or incomplete. NASA, for example, has used Kalman Filters to track and reconstruct the paths of flight vehicles.
By combining sensor data with models of vehicle motion, the system can clean noisy data, accurately reconstruct the actual flight trajectory, and make short-term predictions about where the vehicle will go next. This makes the filters very useful for real-time tracking and control in many fields.
While the Kalman Filter itself isn’t a computer vision algorithm, it’s commonly used in vision-based systems to track moving objects, such as a ball, across video frames. It works by predicting where the object will appear next, comparing that prediction with actual detection data, and then updating its estimate to stay accurate. This continuous prediction and correction cycle makes it effective for real-time tracking, even when the object briefly disappears from view or changes speed suddenly.
ByteTrack
ByteTrack is a real-time object tracking algorithm used to follow moving objects such as balls across video frames. It works by detecting objects in each frame and linking detections of the same object over time, creating a continuous sequence called a tracklet.
A tracklet is a short series of frames that shows where the object has been and how it moves. This linking process helps the system keep a consistent identity for each object throughout the video.
Unlike older tracking methods that discard low-confidence detections, ByteTrack keeps and rechecks them. This means it can maintain smooth and stable tracking even when objects move quickly, become blurry, or are briefly hidden. While ByteTrack isn’t an object detector on its own, it’s often used together with computer vision models like Ultralytics YOLOv8 or YOLO11 to track objects accurately and reliably in real time.
BoT-SORT
BoT-SORT is an advanced multi-object tracking algorithm that improves how systems follow moving objects across video frames. It builds on ByteTrack and adds key features like camera motion compensation and object re-identification.
Camera motion compensation helps correct for camera movement such as panning or shaking, while re-identification lets the system recognize and keep tracking the same object even after it leaves and reappears in view. Like ByteTrack, it is also used in combination with models like YOLOv8 or YOLO11.
Common trajectory prediction methods
Now that we’ve seen how tracking works and how it connects to predicting the trajectory of a ball, let’s take a closer look at the main types of trajectory prediction algorithms and methods used to estimate a ball’s motion.
Physics-based kinematic modeling
Physics-based kinematic modeling predicts a ball’s trajectory using the basic laws of motion. It considers factors like initial velocity of the ball, launch angle, gravity, air resistance, and spin to calculate its flight path. By combining these factors, the model estimates where the ball will be, how fast it will move, and in which direction at any moment.
For example, in sports like baseball or golf, it helps predict how far and in what direction the ball will travel based on swing speed and launch angle. While highly accurate in controlled environments, it can struggle with unpredictable real-world conditions like wind, bounces, or player interactions and cause prediction errors.
When combined with computer vision, physics model-based methods enable practical applications in sports analytics and officiating. Hawk-Eye, for instance, is a multi-camera vision system that reconstructs a ball’s 3D trajectory in real time.
It uses several synchronized high-speed cameras to capture the ball’s position from multiple angles and applies triangulation, kinematic equations, and interpolation methods to estimate its path. This integration supports precise tracking and decision-making in sports, including tennis, cricket, and soccer, where trajectory accuracy is essential for line-calling and performance analysis.
Machine learning regression models
Machine learning regression models can predict a ball’s future position or trajectory without relying on explicit physical equations. Instead, they learn patterns from data such as position, velocity, spin, and angle collected through video-based tracking systems or sensors.
When combined with computer vision tracking algorithms, the vision system captures the ball’s position across video frames. This positional data becomes the input for the regression model, which learns how the ball’s motion changes over time. Once trained, the model can predict the ball’s next position or overall path based on what it has already observed.
For example, with respect to table tennis robots or ping pong robot systems, regression models trained on thousands of tracked trajectories have been shown to predict the ball’s flight and bounce patterns in real time. This lets the robot position itself or react quickly to catch or return the ping-pong ball or table tennis ball. Because these models learn directly from real-world examples, they often handle unpredictable effects, like spin, air drag, or surface contact, better than purely physics-based methods.
Fig 3. How a ping-pong robot predicts a ball’s trajectory. (Source)
Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs)
Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) are AI models designed to process data that changes over time, such as motion or other time-series information. They can remember what happened earlier, which helps them recognize movement patterns and predict what might happen next.
In ball trajectory prediction, these models learn how a ball’s past positions, speeds, and spins influence where it will go next. When combined with computer vision and object tracking, a vision system can capture the positions of the ball across frames. An RNN can then use this sequence of positions to predict the ball’s next move or complete path.
Also, LSTMs are especially impactful because they can remember information over longer periods. This enables them to follow a ball’s full motion even when it moves quickly or changes direction.
Recent research has taken this idea a step further. Scientists have developed Hierarchical Bi-LSTM models that can predict ball trajectories in team sports by analyzing how players move and interact with one another. These models can even estimate the ball’s position when it is temporarily hidden or out of view, using patterns in player behavior to infer where the ball is most likely to be.
Real-world applications of ball trajectory prediction
With a better understanding of computer vision and ball trajectory prediction, let’s explore its practical applications in areas such as sports, rehabilitation, and robotics.
Ball trajectory prediction in sports
Understanding how a ball moves and where it will land is crucial in many sports. Using information from cameras and sensors, AI models can predict the ball’s path and initial velocity, helping sports teams anticipate plays, improve techniques, and make smarter decisions.
It also supports player training by analyzing factors like spin or release angle, helping athletes improve performance and avoid injury. Many cutting-edge sports training systems now use ball trajectory prediction to enhance skill development and strategy.
For example, researchers have developed a computer vision system for tennis robots that can detect and predict a tennis ball’s path. Using stereo cameras and convolutional neural networks, the system helps the robot see the ball in three dimensions (x-axis, y-axis, and z-axis) and respond more accurately during play. This can be a game-changing tool for training players for competitions.
Fig 4. Red shows the simulated path; blue shows the robot’s ball trajectory prediction. (Source)
Pros and cons of computer vision-based ball trajectory prediction
Here are some key advantages of using computer vision-based ball trajectory prediction systems:
Real-time forecasting: These systems can accurately predict a ball’s future path instantly, even at high speeds.
Non-invasive: They can operate solely on video input, without requiring physical sensors or markers.
Scalable and adaptable: Vision-based ball trajectory prediction systems can be applied across different sports and environments using standard camera setups.
While computer vision-based ball trajectory prediction has many benefits, there are also some limitations to consider. Here are some factors to keep in mind:
Occlusion challenges: Predictions can often become less accurate when a ball is hidden behind players or other objects.
Lighting and visibility issues: The performance of these systems can be low under poor lighting or complex backgrounds, such as at night.
High computational demand: Processing high-frame-rate video and deep learning models requires powerful hardware that can be expensive.
Key takeaways
Predicting a ball’s trajectory with Vision AI brings together physics, computer vision, and machine learning to understand and forecast motion with remarkable accuracy. From analyzing sports performance to aiding patient recovery and industrial automation systems, trajectory prediction turns visual data into actionable insight. As technology continues to advance, it’s paving the way for smarter, faster, and more adaptive methods that enhance performance, safety, and decision-making.
