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Moving Object Detection using Transformers

Supervisor

Suitable for

Abstract

Moving Object Detection using Transformers Transformers have become a popular choice for many machine learning applications. They have been used with great success for NLP tasks and have more recently been used in the vision domain to tackle object detection in an end-to-end fashion [1]. The aim of this project is to extend the work of [1] and apply it to the moving object detection problem. In a typical object detection framework, the goal is to identify and localise all of the objects of interest in a single image. The moving object detection problem is a generalisation where the goal is to identify and localise only the moving objects in a sequence of images. For example, this would allow a model to distinguish moving vehicles from parked vehicles. You will have access to Calipsa’s real-world CCTV dataset to train and test models. The dataset is challenging since the image sequences are temporally sparse and the time difference between consecutive frames is variable. The image quality is also highly variable. [1] End-to-End Object Detection with Transformers, Carion et al. https://arxiv.org/pdf/2005.12872.pdf"

Prerequisites:

Strong Python Coder, experience with TensorFlow, experience with deep learning especially computer vision applications.