Michael Abramov

Annotated video training data plays an important role in the development of computer vision based AI models for robotics applications. By labeling each frame of video training data annotators create vital contextual information that allows AI systems to function effectively on busy production lines and in crowded warehouses. Video annotation

Security systems powered by computer vision based AI models have the potential to make public spaces safer and private property more secure. By automating aspects of security monitoring, AI equipped cameras act as a force multiplier for security staff, ensuring that effective surveillance is in place at all times. However,

Most image and video training data for machine learning is collected from large open sources like Imagenet. These online repositories allow developers to assemble large pools of data that can then be annotated with labels and categories, creating functional datasets that power computer vision based applications. Whilst data collection resources

The ultimate goal for AI technology in the automotive industry is full automation of all driving functions. Years of development, innovation, and cultural change may still be required before this dream is realised. Despite these long term challenges, right now computer vision based AI models are improving the driving experience

Drone based computer vision AI systems operate in a wide range of environments and perform a variety of important functions. Automated drone systems can be used to monitor livestock, chart the growth of agricultural crops, and even find missing people in the aftermath of natural disasters. In each use case

The world of professional sports is dynamic and diverse. Computer vision based AI models have shown the capacity to capture the changing states of player movements and game flows, enabling coaches and broadcasters to see more and react quicker. In order for AI models to understand movement it is vital