With the advent of Artificial Intelligence (AI) and Deep Neural Networks (DNN), VCA software is being trained to detect, identify, and distinguish various objects in video by exposing them to a large number of tagged examples. In addition to AI-based object classification, computer vision algorithms are also being used to extract data such as absolute speed and size, direction, colour, path, and area. This data can then be searched to concentrate the video analytics effort on relevant information.
In the last decade, with the availability of a significant amount of data and increased computational power, experts have been able to take the theoretical ideas of deep learning and put them to practical use, specifically in the domain of computer vision.
In the last decade, with the availability of a significant amount of data and increased computational power, experts have been able to take the theoretical ideas of deep learning and put them to practical use, specifically in the domain of computer vision.
With the advent of Artificial Intelligence (AI) and Deep Neural Networks (DNN), VCA software is being trained to detect, identify, and distinguish various objects in video by exposing them to a large number of tagged examples.
With the advent of Artificial Intelligence (AI) and Deep Neural Networks (DNN), VCA software is being trained to detect, identify, and distinguish various objects in video by exposing them to a large number of tagged examples.
With the advent of Artificial Intelligence (AI) and Deep Neural Networks (DNN), VCA software is being trained to detect, identify, and distinguish various objects in video by exposing them to a large number of tagged examples.