OpenCV 4 with Python Blueprints, Second Edition by Dr. Menua Gevorgyan

Author:Dr. Menua Gevorgyan [Dr. Menua Gevorgyan]
Language: eng
Format: epub, mobi
Tags: COM016000 - COMPUTERS / Computer Vision and Pattern Recognition, COM051360 - COMPUTERS / Programming Languages / Python, COM087020 - COMPUTERS / Desktop Applications / Design and Graphics
Publisher: Packt Publishing
Published: 2020-03-19T15:32:27+00:00

_, img_objects = cv2.threshold(saliency,

thresh_value, 255, thresh_type)

return img_objects

The resulting proto-objects mask looks as follows:

The proto-objects mask then serves as an input to the tracking algorithm, which we will see in the next section.

Understanding mean-shift tracking

So far we used the salience detector discussed previously to find bounding boxes of proto-objects. We could simply apply the algorithm to every frame of a video sequence and get a good idea of the location of the objects. However, what is getting lost is correspondence information.

Imagine a video sequence of a busy scene, such as from a city center or a sports stadium. Although a saliency map could highlight all the proto-objects in every frame of a recorded video, the algorithm would have no way to establish a correspondence between proto-objects from the previous frame and proto-objects in the current frame.

Also, the proto-objects map might contain some false positives, and we need an approach to select the most probable boxes that correspond to real-world objects. Such false positives can be noticed in the following example:

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