Moving Object Detection: A weakly-Supervised Approach

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Abstract

The goal of moving object detection 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. Typically, object detectors (moving or otherwise) are trained using highly supervised data. Human annotators draw boxes around each object in each frame of an image sequence. This is time consuming and expensive. Similarly to [1], the aim of this project is to explore the possibility of using a weaker level of supervision (image level labels, rather than boxes) to identify and locate moving objects. 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] Is object localization for free? – Weakly-supervised learning with convolutional neural networks, Sivic et al. https://www.di.ens.fr/~josef/publications/Oquab15.pdf Prerequisites:

"Requirements: Strong python coder, experience with Tensorflow, experience with deep learning, especially computer vision applications."