Popular algorithms used to perform object detection include convolutional neural networks (R-CNN, Region-Based Convolutional Neural Networks), Fast R-CNN, and YOLO (You Only Look Once). The R-CNNs are in the R-CNN family, while YOLO is part of the single-shot detector family.

Object Recognition is a technology that lies under the broader domain of Computer Vision. This technology is capable of identifying objects that exist in images and videos and tracking them.

Haar cascade: Basically, the Haar cascade technique is an approach based on machine learning where we use a lot of positive and negative images to train the classifier to classify between the images. Haar cascade classifiers are considered as the effective way to do object detection with the OpenCV library.

YOLO makes less than half the number of background errors as compared to Faster R-CNN. YOLO architecture enables end-to-end training and real-time speed while maintaining high average precision. Faster R-CNN offers end-to-end training as well but involves much more steps as compared to YOLO.