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In machine learning, we use the word “online” to refer to algorithms that are trained on the fly at run time.
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.
If the object was very simple and did not change it’s appearance much, we could use a simple template as an appearance model and look for that template. The appearance of an object can change dramatically. The job of the classifier is to classify a rectangular region of an image as either an object or background.
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 that just the motion of the object.
Update: In Open CV 3.3, the tracking API has changed.
Object Tracking Thesis
The code checks for the version and then uses the corresponding API.
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. C import cv2 import sys (major_ver, minor_ver, subminor_ver) = (cv2.__version__).split('.')￼ if __name__ == '__main__' : # Set up tracker.
# Instead of MIL, you can also use tracker_types = ['BOOSTING', 'MIL','KCF', 'TLD', 'MEDIANFLOW', 'GOTURN', 'MOSSE', 'CSRT'] tracker_type = tracker_types if int(minor_ver) In this section, we will dig a bit into different tracking algorithms.
While building an online classifier, we do not have the luxury of having thousands of examples of the positive and negative classes.
Let’s look how different tracking algorithms approach this problem of online training.