If you do not have the time to read the entire post, just watch this video and learn the usage in this section. But if you really want to learn about object tracking, read on. Simply put, locating an object in successive frames of a video is called tracking. The definition sounds straight forward but in computer vision and machine learning, tracking is a very broad term that encompasses conceptually similar but technically different ideas.
For example, all the following different but related ideas are generally studied under Object Tracking. If you have ever played with OpenCV face detection, you know that it works in real-time and you can easily detect the face in every frame. So, why do you need tracking in the first place? OpenCV 4 comes with a tracking API that contains implementations of many single object tracking algorithms.
There are 8 different trackers available in OpenCV 4. Note : OpenCV 3. OpenCV 3. Update : In OpenCV 3. The code checks for the version and then uses the corresponding API. Before we provide a brief description of the algorithms, let us see the setup and usage. We then open a video and grab a frame.
We define a bounding box containing the object for the first frame and initialize the tracker with the first frame and the bounding box. Finally, we read frames from the video and just update the tracker in a loop to obtain a new bounding box for the current frame.
Results are subsequently displayed. In this section, we will dig a bit into different tracking algorithms. The goal is not to have a deep theoretical understanding of every tracker, but to understand them from a practical standpoint. Let me begin by first explaining some general principles behind tracking. In tracking, our goal is to find an object in the current frame given we have tracked the object successfully in all or nearly all previous frames.
Since we have tracked the object up until the current frame, we know how it has been moving. In other words, we know the parameters of the motion model. If you knew nothing else about the object, you could predict the new location based on the current motion model, and you would be pretty close to where the new location of the object is.
But we have more information than just the motion of the object. We know how the object looks in each of the previous frames. In other words, we can build an appearance model that encodes what the object looks like. This appearance model can be used to search in a small neighborhood of the location predicted by the motion model to more accurately predict the location of the object. The motion model predicts the approximate location of the object.
The appearance model fine tunes this estimate to provide a more accurate estimate based on appearance. Mia downloaded an app You've earned 20 points. Got a few minutes to spare while waiting in line? Make your time more rewarding with FeaturePoints - we've paid millions of dollars in rewards to our users. Win Instant Contests Scratch to win 50, points instantly! Available in our iOS and Android apps.
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