Object Detection and Tracking with Python Opencv [Bangla] || How to Tracking Object in Python 2021
In this video we are going to learn How to object tracking with python opencv. After watching this video You can detect and Tracking any
specific object. This is a fairly simple tutorial so it should be easy to follow. In this tutorial i explain with Bangla.
source Code: https://github.com/arafatHoshen/OpenC...
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0:00:00 Introduction
0:00:24 Intro
0:00:32 Open IDE
0:01:05 Install module
0:02:06 Start Coding
0:04:08 fps Count code
0:09:40 Draw Rectangle
0:15:12 The End & Subscribe
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How to object tracking in python opencv ?
1.Taking an initial set of object detections (such as an input set of bounding box coordinates)
2.Creating a unique ID for each of the initial detections.
3.And then tracking each of the objects as they move around frames in a video, maintaining the assignment of unique IDs.
What is Object Tracking?
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:
1.Dense Optical flow: These algorithms help estimate the motion vector of every pixel in a video frame.
2.Sparse optical flow: These algorithms, like the Kanade-Lucas-Tomashi (KLT) feature tracker, track the location of a few feature points in an image.
3.Kalman Filtering: A very popular signal processing algorithm used to predict the location of a moving object based on prior motion information.
One of the early applications of this algorithm was missile guidance! Also as mentioned here, “the on-board computer that guided the descent
of the Apollo 11 lunar module to the moon had a Kalman filter”.
4.Meanshift and Camshift: These are algorithms for locating the maxima of a density function. They are also used for tracking.
5.Single object trackers: In this class of trackers, the first frame is marked using a rectangle to indicate the location of the object we
want to track. The object is then tracked in subsequent frames using the tracking algorithm. In most real-life applications, these trackers
are used in conjunction with an object detector.
6.Multiple object track finding algorithms: In cases when we have a fast object detector, it makes sense to detect multiple objects in each
frame and then run a track finding algorithm that identifies which rectangle in one frame corresponds to a rectangle in the next frame.
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