CV Canvas

Very often the area you want to track is too big for one camera to cover. In this case, the first thought is usually to run computer vision algorithms on each camera separately as shown below: However, the approach above, while there is nothing wrong with it, is not the most efficient and is definitely [...]Continue reading

Find circles and contours

Lightact has 2 computer vision nodes that try to find features in an incoming cvMat. They are Find CV circles and Find CV contours. In short, Find CV Circles finds minimum enclosing circles of all the blobs it finds in Source as long as the radius is between Min circle and Max circle inputs. It [...]Continue reading

Background subtraction (MOG2)

Background subtraction (also known as Foreground detection) is a computer vision algorithm that tries to distinguish foreground objects from the background. There are various approaches to this problem, however, Lightact uses an approach called MOG2 (if you want to delve deeper, check out OpenCV's BackgroundSubtractorMOG2 class). Background subtraction can be performed using MOG2 Background Subtraction node. The [...]Continue reading

Computer vision optimisation

Most computer vision nodes (the nodes which have a cvMat input & output) can be quite a drain on system resources, so care should be taken not to unnecessarily burden the system. In these chapters we'll explain briefly what you can do to optimize the processes. cvMat resolution By far the most important parameter is the [...]Continue reading

Optical flow

Optical flow is a computer vision algorithm which, in a nutshell, analyzes the movement of pixels from frame to frame. As such it is particularly useful where you've got background movement which doesn't allow you to use other methods. It is one of the quickest computer vision algorithms to set up although it is computationally [...]Continue reading