Priyanka Bharadwaj

Research Student -  PhD

International Centre for Neuromorphic Systems

Thesis Title

Automatic Real-Time Recognition of Objects in Photos and Videos on Edge Devices

Research Project

Neutral networks, especially deep neural networks, is the major class of machine learning techniques that are increasingly being used in real-time object detection. Classification of objects, static region detection, and clip and key-frame detection errors needs to be accounted while detecting real time objects. Much of the current work in this area has been in the domain of object classification. Recently, there is an increased interest in a new approach to detect object using YOLO. Object detection is considered as a regression problem to spatially separate bounding boxes and associated class probabilities. A single neural network predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is considered as a single network, it can be optimized end-to-end directly on detection performance, which is the main focus of this project.


  • BE in Electronics and Communication from Visvesvaraya Technological University, Karnataka, India
  • MS in Electrical and Computer Engineering from the State University of New York, NY, USA

LocationWestern Sydney University Werrington South campus (Bullecourt Ave, Milperra)