Contributions to the Problem of Fight Detection in Video

While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in camera smartphones. The clear practical applications have led to a surge of interest in developing violence detectors

Contributions to the Problem of Fight Detection in Video

While action detection has become an important line of research in computer vision, the detection of particular events such as violence, aggression or fights, has been relatively less studied. These tasks may be extremely useful in several video surveillance scenarios such as psychiatric wards, prisons or even in camera smartphones. The clear practical applications have led to a surge of interest in developing violence detectors

Deep learning for activity recognition using audio and video

Neural networks have established themselves as powerhouses in what concerns several types of detection, ranging from human activities to their emotions. Several types of analysis exist, and the most popular and successful is video. However, there are other kinds of analysis, which, despite not being used as often, are still promising. In this article, a comparison between audio and video analysis is drawn in an attempt to classify violence detection in real-time streams. This study, which followed the CRISP-DM methodology, made use of several models available through PyTorch in order to test a diverse set of models and achieve robust results. The results obtained proved why video analysis has such prevalence, with the video classification handily outperforming its audio classification counterpart. Whilst the audio models attained on average 76% accuracy, video models secured average scores of 89%, showing a significant difference in performance. This study concluded that the applied methods are quite promising in detecting violence, using both audio and video.This work has been supported by FCT-Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and the project "Integrated and Innovative Solutions for the well-being of people in complex urban centers" within the Project Scope NORTE-01-0145-FEDER000086. C.N. thank the FCT-Fundacao para a Ciencia e Tecnologia for the grant 2021.06507.BD

Inflated 3D ConvNet context analysis for violence detection

According to the Wall Street Journal, one billion surveillance cameras will be deployed around the world by 2021. This amount of information can be hardly managed by humans. Using a Inflated 3D ConvNet as backbone, this paper introduces a novel automatic violence detection approach that outperforms state-of-the-art existing proposals. Most of those proposals consider a pre-processing step to only focus on some regions of interest in the scene, i.e., those actually containing a human subject. In this regard, this paper also reports the results of an extensive analysis on whether and how the context can affect or not the adopted classifier performance. The experiments show that context-free footage yields substantial deterioration of the classifier performance (2% to 5%) on publicly available datasets. However, they also demonstrate that performance stabilizes in context-free settings, no matter the level of context restriction applied. Finally, a cross-dataset experiment investigates the generalizability of results obtained in a single-collection experiment (same dataset used for training and testing) to cross-collection settings (different datasets used for training and testing)

Understanding egocentric human actions with temporal decision forests

Understanding human actions is a fundamental task in computer vision with a wide range of applications including pervasive health-care, robotics and game control. This thesis focuses on the problem of egocentric action recognition from RGB-D data, wherein the world is viewed through the eyes of the actor whose hands describe the actions. The main contributions of this work are its findings regarding egocentric actions as described by hands in two application scenarios and a proposal of a new technique that is based on temporal decision forests. The thesis first introduces a novel framework to recognise fingertip writing in mid-air in the context of human-computer interaction. This framework detects whether the user is writing and tracks the fingertip over time to generate spatio-temporal trajectories that are recognised by using a Hough forest variant that encourages temporal consistency in prediction. A problem with using such forest approach for action recognition is that the learning of temporal dynamics is limited to hand-crafted temporal features and temporal regression, which may break the temporal continuity and lead to inconsistent predictions. To overcome this limitation, the thesis proposes transition forests. Besides any temporal information that is encoded in the feature space, the forest automatically learns the temporal dynamics during training, and it is exploited in inference in an online and efficient manner achieving state-of-the-art results. The last contribution of this thesis is its introduction of the first RGB-D benchmark to allow for the study of egocentric hand-object actions with both hand and object pose annotations. This study conducts an extensive evaluation of different baselines, state-of-the art approaches and temporal decision forest models using colour, depth and hand pose features. Furthermore, it extends the transition forest model to incorporate data from different modalities and demonstrates the benefit of using hand pose features to recognise egocentric human actions. The thesis concludes by discussing and analysing the contributions and proposing a few ideas for future work.Open Acces

Learning visual representations with deep neural networks for intelligent transportation systems problems

Esta tesis se centra en dos grandes problemas en el área de los sistemas de transportes inteligentes (STI): el conteo de vehículos en escenas de congestión de tráfico; y la detección y estimación del punto de vista, de forma simultánea, de los objetos en una escena. Respecto al problema del conteo, este trabajo se centra primero en el diseño de arquitecturas de redes neuronales profundas que tengan la capacidad de aprender representaciones multi-escala profundas, capaces de estimar de forma precisa la cuenta de objetos, mediante mapas de densidad. Se trata también el problema de la escala de los objetos introducida por la gran perspectiva típicamente presente en el área de recuento de objetos. Además, con el éxito de las redes hourglass profundas en el campo del conteo de objetos, este trabajo propone un nuevo tipo de red hourglass profunda con conexiones de corto circuito auto-gestionadas. Los modelos propuestos se evalúan en las bases de datos públicas más utilizadas y logran los resultados iguales o superiores al estado del arte en el momento en que fueron publicadas. Para la segunda parte, se realiza un estudio comparativo completo del problema de detección de objetos y la estimación de la pose de forma simultánea. Se expone el compromiso existente entre la localización del objeto y la estimación de su pose. Un detector necesita idealmente una representación que sea invariable al punto de vista, mientras que un estimador de poses necesita ser discriminatorio. Por lo tanto, se proponen tres nuevas arquitecturas de redes neurales profundas en las que el problema de la detección de objetos y la estimación de la pose se van desacoplando progresivamente. Además, se aborda la cuestión de si la pose debe expresarse como un valor discreto o continuo. A pesar de ofrecer un rendimiento similar, los resultados muestran que los enfoques continuos son más sensibles al sesgo del punto de vista principal de la categoría del objeto. Se realiza un análisis comparativo detallado en las dos bases de datos principales, es decir, PASCAL3D+ y ObjectNet3D. Se logran resultados competitivos con todos los modelos propuestos en ambos conjuntos de datos

Multimedia Forensics

This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field