RCOS: Real time context sharing across a fleet of smart mobile devices

Today, biodiversity is endangered by the currently applied intensive farming methods imposed on food producers by intermediate actors (e.g.: retailers). The lack of a direct communication technology between the food producer and the consumer creates dependency on the intermediate actors for both producers and the consumers. A tool allowing producers to directly and efficiently market produce that meets customer demands could greatly reduce the dependency enforced by intermediate actors. To this end, in this thesis, we propose, develop, implement and validate a Real Time Context Sharing (RCOS) system. RCOS takes advantage of the widely used publish/subscribe paradigm to exchange messages between producers and consumers, directly, according to their interest and context. Current systems follow a topic-based model or a content-based model. With RCOS, we propose a context-awareness approach into the matching process of publish/subscribe paradigm. Finally, as a proof of concept, we extend the Apache ActiveMQ Artemis software and create a client prototype. We evaluate our proof of concept for larger scale deployment.

Deep convolutional neural networks for semantic video object segmentation

In this thesis, we propose to infer pixel-level labelling in video by utilising only object category information, exploiting the intrinsic structure of video data. Our motivation is the observation that image-level labels are much more easily to be acquired than pixel-level labels, and it is natural to find a link between the image level recognition and pixel level classification in video data, which would transfer learned recognition models from one domain to the other one. To this end, this thesis proposes two domain adaptation approaches to adapt the deep convolutional neural network (CNN) image recognition model trained from labelled image data to the target domain exploiting both semantic evidence learned from CNN, and the intrinsic structures of unlabelled video data. Our proposed approaches explicitly model and compensate for the domain adaptation from the source domain to the target domain which in turn underpins a robust semantic object segmentation method for natural videos. We demonstrate the superior performance of our methods by presenting extensive evaluations on challenging datasets comparing with the state-of-the-art methods.