In traditional algorithms, the developer’s knowledge gets implemented as a program, which analyzes some input data to evaluate a result. Artificial Intelligence (AI) and particularly its sub-field – machine learning (ML) establish a way to let the machine to learn by itself, based on a set of input data and known results. After this learning (or training) process, the machine is able to analyze the input data and suggest a result, based on the knowledge, gathered during the training. The developer does not need to understand and decide how exactly the algorithm works. One just has to know how to train the machine.

Theoretically, ML algorithms can achieve better results than a human-designed algorithm, at the expense of much higher computational power. Contemporary processors already have the necessary power which enables ML algorithms to be used even for image processing.

The most popular field where AI/ML algorithms prove to be much better than their predecessors is image classification and objects detection in images. Using AI/ML as object detectors is a key milestone in reaching the human brain power of automated scene analysis and taking the consequent decisions.


AI technology using convolutional neural network to detect and segment people in a cluttered environment.
Rectangle 1617
Can run on GPU or DSP to save power
Rectangle 1617
The neural network can be re-trained for Customer specific objects instead of people
Rectangle 1617

The background can be replaced or blurred, creating a variety of effects – Single camera Bokeh/ Background blur, Black & White background, Background replacement etc.


new bkg

Background replacement


Bokeh / Background Blur


Black and White

If you are considering introducing our technology or products, or if you are interested in co-creation with us, please feel free to contact us from here.