On my GPU (Nvidia GTX 1060Ti 6Gb Ram) it ran at 2 fps. 📹 Inside the documentation specify that it was able to run at 22fps! Allowing Real Time Capability BUT was not so correct. This led me to the last experiment with a custom tracker specially solving the fast moving objects based on heatmaps this next development is promising because the nearly perfect detections on tennis (which is a smaller ball and moves faster!) 3rd TrackNet We find out that when the ball is in still position it is able to be detected by Detectron2 and with a high confidence score.īut this pretrained neural network has problems with fast moving objects like the ball. This is the video I will use to analyze with 3 options of ball detectors.īall object is detected with good precision only standing still or in slow motion. I successfully want to track the ball on the following video, for sure my expectations were not too high, I’m aware the ball moves incredible fast.
#Cricket 3d ball tracking code
To make this I use a “plug and play” approach, I don’t modify the code of the detectors and neural networks I find, I just download and run it with my local example with only minor modifications. My objective was to try out the ball detectors I come across within 5 hours of research in Google to let this experiment very plain and without an insider view of the AI industry. Let’s begin ! - My journey into ball detection Or get lost in the rapid and sometimes erratic movement of people (players) and objects (ball, net, etc.) for an AI to compete with a human is difficult. False positives, same objects outside the game (Players with the same outfit on the benches, sports balls outside the game identical to the ball “in game”)īut we humans are magnificent at this tasks! We as humans negate effectively the camera movement that's why we don't get confused between the background and playing field for example….The background mixing with players and ball, objects and people with nearly identical appearance.The amount of objects and people moving in every direction with different speeds and sometimes unpredictable trajectories.The camera is constantly panning, zooming and sometimes tilting and sometimes a replay appears from a radical different angle and our brains are SO good at negating this motion that we don’t even notice it.Luckily for Ghana, the referee was able to spot it and give them the penalty and a last chance to qualify to the semi-finals. It is very hard to notice Suarez was actually touching the ball with his hand to stop the goal. Look at how many crazy things happen on the field on this 2010 World Cup.
Sometimes, even a human struggles to understand what’s happening on a complex sports play. For us to develop and teach an AI to to watch a football game and detect elements or events from the match sounds like an easy task but its is actually very challenging. The capability to track moving objects and analyze them plays a crucial role. Sports is equivalent to motion, people and objects moving in different speeds and directions. Introduction: Tracking and Detecting Moving ObjectsĪs we stated before detect and track moving objects are key tasks for an AI in order to succeed in and thrive in this niche. Only being able to detect the ball won't take you there but we believe with a good ball detector alongside the actual people detector from pre-trained neural networks is the quickest way to start. even less at high speeds… That's why we created this post to take evaluate the quickest way possible to ball detection. Having a successful Sports AI is a result of a well done computer vision set of detections, nowadays there are many available neural networks good at detecting human bodies (like OpenPose for example) so players will be easy to detect but not so the ball. If you can detect the ball (even at high speeds) then you will be able to detect easier key events of the game, metrics and so much more useful information.
If you are in the AI Sports niche is totally relevant for you to know the state of the art in ball detection, this object is always the main center of attention of any sport. đźš©W hy should you care? and Why the ball?
(I’m not the technician on my team meaning the point of view provided in this post won’t take you deep into any algorithm or neural network). Quickly we find out that detecting the ball is key component to develop a robust Sports AI. We want to evaluate the quickest way to detect the ball in a sport event in order to develop an Sports AI without spending a million dollars on tech or developers. A quick view to the available solutions for automatic ball detection 🚩 Objective
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