Machine Learning

Mobile Application

Intelligent Stereo Vision

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  • Intelligent analysis of 3D environments using custom-built stereo vision cameras and an AI component hosted within the camera.
  • Predefined rules are available to identify scenarios such as unattended luggage and unattended children.
  • Ability to measure objects live in 3 dimensions.
  • The camera is built using Intel’s Realsense and Nvidia’s Jetson component which has a built-in GPU.
  • On-device machine learning capabilities enable the solution to dodge network challenges.
  • Involves a dynamic rule engine where rules can be written for each scenario that needs to be tracked and the camera shall automatically identify such scenarios.
  • Python 3 based application to read camera stream using Realsense SDK.
  • Preprocessing of frames performed using OpenCV and Leptonica based image processing.
  • The rule engine was developed using Lex and Yacc. Rules were parsed and converted into a state machine.
  • Rule evaluation was performed while streaming through the video frames and making state transitions.
  • Yolo-V3 based image classification was developed and the model was extended for newer entity types.
  • 3D measurements were performed using a homegrown algorithm written in python. This involved StereoDNN neural network based modeling and prediction.
  • Collision Detection was performed using a collider based overlap methodology.
  • Temporality was maintained in order to be evaluated in the rules.
  • An Android application was developed to control and deploy the rules onto the camera.
  • Python 3
  • Jetson SDK
  • Intel Realsense SDK
  • Android
  • Docker
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