From a Visual Scene to a Virtual Representation: A Cross-Domain Review

The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including surveillance, sports, retail, entertainment, and intelligent vehicles. Despite significant advances in analyzing and extracting data from images and video, there is a lack of solutions able to analyze and semantically describe the information in the visual scene so that it can be efficiently used and repurposed. Scientific contributions have focused on individual aspects or addressing specific problems and application areas, and no cross-domain solution is available to implement a complete system that enables information passing between cross-cutting algorithms. This paper analyses the problem from an end-to-end perspective, i.e., from the visual scene analysis to the representation of information in a virtual environment, including how the extracted data can be described and stored. A simple processing pipeline is introduced to set up a structure for discussing challenges and opportunities in different steps of the entire process, allowing to identify current gaps in the literature. The work reviews various technologies specifically from the perspective of their applicability to an endto- end pipeline for scene analysis and synthesis, along with an extensive analysis of datasets for relevant tasks.info:eu-repo/semantics/publishedVersio

LOOKING INTO ACTORS, OBJECTS AND THEIR INTERACTIONS FOR VIDEO UNDERSTANDING

Automatic video understanding is critical for enabling new applications in video surveillance, augmented reality, and beyond. Powered by deep networks that learn holistic representations of video clips, and large-scale annotated datasets, modern systems are capable of accurately recognizing hundreds of human activity classes. However, their performance significantly degrades as the number of actors in the scene or the complexity of the activities increases. Therefore, most of the research thus far has focused on videos that are short and/or contain a few activities performed only by adults. Furthermore, most current systems require expensive, spatio-temporal annotations for training. These limitations prevent the deployment of such systems in real-life applications, such as detecting activities of people and vehicles in an extended surveillance videos. To address these limitations, this thesis focuses on developing data-driven, compositional, region-based video understanding models motivated by the observation that actors, objects and their spatio-temporal interactions are the building blocks of activities and the main content of video descriptions provided by humans. This thesis makes three main contributions. First, we propose a novel Graph Neural Network for representation learning on heterogeneous graphs that encode spatio-temporal interactions between actor and object regions in videos. This model can learn context-aware representations for detected actors and objects, which we leverage for detecting complex activities. Second, we propose an attention-based deep conditional generative model of sentences, whose latent variables correspond to alignments between words in textual descriptions of videos and object regions. Building upon the framework of Conditional Variational Autoencoders, we train this model using only textual descriptions without bounding box annotations, and leverage its latent variables for localizing the actors and objects that are mentioned in generated or ground-truth descriptions of videos. Finally, we propose an actor-centric framework for real-time activity detection in videos that are extended both in space and time. Our framework leverages object detections and tracking to generate actor-centric tubelets, capturing all relevant spatio-temporal context for a single actor, and detects activities per tubelet based on contextual region embeddings. The models described have demonstrably improved the ability to temporally detect activities, as well as ground words in visual inputs