Object_detector = cv2.createBackgroundSubtractorMOG2()Īs you can see in the example code we also used the createBackgroundSubtractorMOG2 function which Returns the “background ratio” parameter of the algorithm and then create the mask.Īs you can see, however, there is a lot of noise in the image. Write me in real-time and we will proceed step by step with the integration of the libraries Object detectionįirst we need to call the highway.mp4 file and create a mask cap = cv2.VideoCapture("highway.mp4") This has already been written and you can simply download itģ main file. What do we need?ġ The video of the highway we will use to count the vehiclesĢ tracker files. Certainly, if you need to design a tri-section of objects this is the tool you need. Surely where having seen the tutorial you will easily think of thousands of ideas applied to real-life or potentially to industry. The possible applications are different for example, counting how many people are in a certain area, checking how many objects pass on a conveyor belt, or counting the vehicles on a highway. We will talk first about object detection and then about how to apply object tracking to the detection. Object tracking does frame-by-frame tracking but keeps the history of where the object is at a time after time Object detection is the detection on every single frame and frame after frame. In this tutorial we will learn how to use Object Tracking with Opencv and Python.įirst of all it must be clear that what is the difference between object detection and object tracking:
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