Scorch Video Recognition Engine

is a software module that allows the analysis of videos and the visual recognition of objects, backgrounds, actions and emotions in their various combinations

The module analyzes live streams and recorded videos generating time and in-video (/stream) location tags. It is able to visually recognize objects, backgrounds, actions and emotions sending them to a receiver unit or saving them as a file so a device can conclude as to what its ecosystem should do next.

Modules can be provided in two configurations

Server configuration:

It is a powerful server module – e.g. it can be deployed on a server with a powerful graphics card capable of processing 600 frames per second – for processing live streams and video files data

Modules can be configured to run either on Linux or Windows. Minimum requirements: 16Gb of memory and 200Gb of disk space.

Embedded configuration:

It is a cross-functional module for (wearable) cameras, drones and mobile phones.

One trained module with custom vision skills - particular scenes, actions, objects, etc. – requires 2-4Gb. The in-module skills are to be specified by a customer so a device will be able to use vision skills without an internet connection while other vision skills are going to be stored on the Neuro Cloud as the entire skills base my be too large to fit into the available memory of a device.

What we include in our work:

  • use of deep learning neural networks for the analysis of video frames – no tradeoff occurs between the amount/quality of information extracted and the video processing speed
  • machine learning algorithms-based analysis of scenes and situations
  • machine learning algorithms-based analysis of human actions, creation of object silhouettes
  • detection of emotions and highlights
  • machine-based inference-making on what happens in a video

Scorch Neuro Cloud 

 is a cloud-based storage for machine vision skills with convenient interfaces.  

The primary purpose of the Cloud is to store the information/machine vision skills amassed through the training of neural networks. This allows customers to easily access and use the skills for their purposes.

The Cloud allows to:

  • teach neural networks to customer-specified scenes using Web tools with visualization features
  • save, update and add acquired vision skills
  • upload the preferred vision skills to devices using customer delivery modules
  • adapt the vision skills base to other deep learning frameworks such as Caffe and Teano
  • exchange vision skills with other Scorch Cloud customers
  • receive updates on the scenes trained by Scorch
  • send new/unknown scenes to the Cloud so they are learned