Description of the project

Robust and efficient Machine Learning (ML) algorithms are the crux of attention in the image/video and speech processing community. The purpose of this project is to develop an intelligent object detection system to categorize various objects of interest in the daily life. Hence, provide a smart vision capability to visually impaired people. Mostly, the currently available supportive tools for vision impairment are based on obstacle avoidance rather than obstacle/object detection and then categorization. In other words, this Robust and efficient Machine Learning (ML) algorithms are the crux of attention in the image/video and speech processing community. The purpose of this project is to develop an intelligent object detection system to categorize various objects of interest in the daily life. Hence, provide a smart vision capability to visually impaired people. Mostly, the currently available supportive tools for vision impairment are based on obstacle avoidance rather than obstacle/object detection and then categorization. In other words, this just avoids the use of white stick but cannot contribute more towards development of a critical sense i.e., vision. The main challenge in this project is to devise a machine learning algorithm that should be capable of recognizing the objects from video/image with high accuracy in a highly variable environment.

This is achievable if the ML system learns and recognizes the object of interest viewed through arbitrary angle and position i.e., learn to capture the unique and abstract representation of object. For example, a cell phone or a cup placed on the table should be recognized regardless of its size, shape and orientation. Not until recently, deep neural architectures have gained a prominent place in the picture of machine learning algorithms. Marked by the ability of extracting characteristic and invariable feature of an object, they are amongst the state-of-the-art in various applications related to image or object recognition/categorization. Our aim is to design a dedicated deep neural architecture and employ that in a form of portable yet wearable device using small video capturing cameras. The end product should give the information about the type and distance of the object from a user. Indeed, this can be realized with conventional image processing together with the well- learned ML system. PI has an active research linkage with the, University of Western Australia, Perth to benefit from their skills. PI has been working with industry as consultant in developing facial recognition system using deep convolution neural networks.