Object Tracking Thesis

The goal is not to have a deep theoretical understanding of every tracker, but to understand them from a practical standpoint.

The goal is not to have a deep theoretical understanding of every tracker, but to understand them from a practical standpoint.

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This way the classifier learns to differentiate what is a cat and what is not.

You can learn more about image classification here.

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.

Jokes aside, the animation demonstrates what we want from an ideal object tracker — speed, accuracy, and robustness to occlusion.

Open CV 3 comes with a new tracking API that contains implementations of many single object tracking algorithms.A classifier is trained by feeding it positive ( object ) and negative ( background ) examples.If you want to build a classifier for detecting cats, you train it with thousands of images containing cats and thousands of images that do not contain cats.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[2] if int(minor_ver) In this section, we will dig a bit into different tracking algorithms.Before we provide a brief description of the algorithms, let us see the setup and usage.In the commented code below we first set up the tracker by choosing a tracker type — BOOSTING, MIL, KCF, TLD, MEDIANFLOW, GOTURN, MOSSE or CSRT. We define a bounding box containing the object for the first frame and initialize the tracker with the first frame and the bounding box.We will also learn the general theory behind modern tracking algorithms.This problem has been perfectly solved by my friend Boris Babenko as shown in this flawless real-time face tracker below!Open CV 3.1 has implementations of these 5 trackers — BOOSTING, MIL, KCF, TLD, MEDIANFLOW.Open CV 3.0 has implementations of the following 4 trackers — BOOSTING, MIL, TLD, MEDIANFLOW.