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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.