Object Detection in a video is to locate the presence of objects, put them under certain classes based on our deep learning model, and place bounding boxes around them. To simply put our Input is a video or images with multiple objects and Output is the same video or image with bounding boxes (of certain height and width) around the objects class names and probabilities to which they belong. Here we will be using a pre-trained YOLO (You Only Look Once) model which was trained for a large dataset of around 80 classes of objects for a long time with high-powered resources. This article, will not go much deeper into the YOLO architecture and focus more on the ImageAI library to do object detection on our video and get the results. Building these models from scratch takes a lot of understanding of mathematics and its architecture along with thousands of lines of code.

Now you can start run the Python file and start the training.

With the rise and popularity of deep learning algorithms, there has been impressive progress in the field of Artificial Intelligence, especially in Computer Vision. The second decade of the 21st century has seen rapid adoption of Convolutional Neural Networks, invention of state-of-the-art algorithms, availability of massive training data and the invention of high performance and cost effective compute. One of the key concepts in Computer Vision is image classification; which is the ability of a software system to label correctly the dominant object in an image. Systems powered by current state-of-the-art algorithms have surpassed human capabilities in identifying objects in images, and this technology is being widely used by technology giants, large businesses and governments for purposes such as intelligent applications, websites, weather, research, surveillance and more. However, the technology, just like the early days of computing and the world wide web is beyond the reach of prospective developers, programmers, small scale businesses and individuals that will largely benefit from it.

Now you can start run the Python file and start the training.

One of the important fields of Artificial Intelligence is Computer Vision. Computer Vision is the science of computers and software systems that can recognize and understand images and scenes. Computer Vision is also composed of various aspects such as image recognition, object detection, image generation, image super-resolution and more. Object detection is probably the most profound aspect of computer vision due the number practical use cases. In this tutorial, I will briefly introduce the concept of modern object detection, challenges faced by software developers, the solution my team has provided as well as code tutorials to perform high performance object detection.