Object Detection Using OpenCV and Python

The object detection works on the Viola-Jones algorithm, which was proposed by Paul Viola and Michael Jones. The aforementioned algorithm is based on machine learning. The first step involves training a cascade function with a large amount of negative and positive labeled images. Once the classifier is trained, identifying features, namely “HAAR Features,” are extracted from these training images. HAAR features are essentially rectangular features with regions of bright and dark pixels. 

How does Object Detection work?

Here we are yolo v3 algorithm for our work . We have pretrained model’s weights and config files with us .

As mentioned already, YOLO which stands for “You only look once” is a single shot detection algorithm which was introduced by Joseph Redmon in May 2016. Although the name of the algorithm may sound strange, it gives a perfect description of this algorithm as it predicts classes and bounding boxes for the whole image in one run of the algorithm.

YOLO performed surprisingly well as compared to the other single-shot detectors of that time in terms of speed and accuracy. It is not the most accurate algorithms when it comes to object detection but certainly, it makes that up with its impressive speed and thus is a good balance between speed and accuracy.