Online Geometric Human Interaction Segmentation and Recognition

The goal of this work is the temporal localization and recognition of binary people interactions in video. Human-human interaction detection is one of the core problems in video analysis. It has many applications such as in video surveillance, video search and retrieval, human-computer interaction, and behavior analysis for safety and security. Despite the sizeable literature in the area of activity and action modeling and recognition, the vast majority of the approaches make the assumption that the beginning and the end of the video portion containing the action or the activity of interest is known. In other words, while a significant effort has been placed on the recognition, the spatial and temporal localization of activities, i.e. the detection problem, has received considerably less attention. Even more so, if the detection has to be made in an online fashion, as opposed to offline. The latter condition is imposed by almost the totality of the state-of-the-art, which makes it intrinsically unsuited for real-time processing. In this thesis, the problem of event localization and recognition is addressed in an online fashion. The main assumption is that an interaction, or an activity is modeled by a temporal sequence. One of the main challenges is the development of a modeling framework able to capture the complex variability of activities, described by high dimensional features. This is addressed by the combination of linear models with kernel methods. In particular, the parity space theory for detection, based on Euclidean geometry, is augmented to be able to work with kernels, through the use of geometric operators in Hilbert space. While this approach is general, here it is applied to the detection of human interactions. It is tested on a publicly available dataset and on a large and challenging, newly collected dataset. An extensive testing of the approach indicates that it sets a new state-of-the-art under several performance measures, and that it holds the promise to become an effective building block for the analysis in real-time of human behavior from video

Simple Mechanisms for Representing, Indexing and Manipulating Concepts

Deep networks typically learn concepts via classifiers, which involves setting up a model and training it via gradient descent to fit the concept-labeled data. We will argue instead that learning a concept could be done by looking at its moment statistics matrix to generate a concrete representation or signature of that concept. These signatures can be used to discover structure across the set of concepts and could recursively produce higher-level concepts by learning this structure from those signatures. When the concepts are `intersected', signatures of the concepts can be used to find a common theme across a number of related `intersected' concepts. This process could be used to keep a dictionary of concepts so that inputs could correctly identify and be routed to the set of concepts involved in the (latent) generation of the input.Comment: 19 page

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Identification, synchronisation and composition of user-generated videos

Cotutela Universitat Politècnica de Catalunya i Queen Mary University of LondonThe increasing availability of smartphones is facilitating people to capture videos of their experience when attending events such as concerts, sports competitions and public rallies. Smartphones are equipped with inertial sensors which could be beneficial for event understanding. The captured User-Generated Videos (UGVs) are made available on media sharing websites. Searching and mining of UGVs of the same event are challenging due to inconsistent tags or incorrect timestamps. A UGV recorded from a fixed location contains monotonic content and unintentional camera motions, which may make it less interesting to playback. In this thesis, we propose the following identification, synchronisation and video composition frameworks for UGVs. We propose a framework for the automatic identification and synchronisation of unedited multi-camera UGVs within a database. The proposed framework analyses the sound to match and cluster UGVs that capture the same spatio-temporal event, and estimate their relative time-shift to temporally align them. We design a novel descriptor derived from the pairwise matching of audio chroma features of UGVs. The descriptor facilitates the definition of a classification threshold for automatic query-by-example event identification. We contribute a database of 263 multi-camera UGVs of 48 real-world events. We evaluate the proposed framework on this database and compare it with state-of-the-art methods. Experimental results show the effectiveness of the proposed approach in the presence of audio degradations (channel noise, ambient noise, reverberations). Moreover, we present an automatic audio and visual-based camera selection framework for composing uninterrupted recording from synchronised multi-camera UGVs of the same event. We design an automatic audio-based cut-point selection method that provides a common reference for audio and video segmentation. To filter low quality video segments, spatial and spatio-temporal assessments are computed. The framework combines segments of UGVs using a rank-based camera selection strategy by considering visual quality scores and view diversity. The proposed framework is validated on a dataset of 13 events (93~UGVs) through subjective tests and compared with state-of-the-art methods. Suitable cut-point selection, specific visual quality assessments and rank-based camera selection contribute to the superiority of the proposed framework over the existing methods. Finally, we contribute a method for Camera Motion Detection using Gyroscope for UGVs captured from smartphones and design a gyro-based quality score for video composition. The gyroscope measures the angular velocity of the smartphone that can be use for camera motion analysis. We evaluate the proposed camera motion detection method on a dataset of 24 multi-modal UGVs captured by us, and compare it with existing visual and inertial sensor-based methods. By designing a gyro-based score to quantify the goodness of the multi-camera UGVs, we develop a gyro-based video composition framework. A gyro-based score substitutes the spatial and spatio-temporal scores and reduces the computational complexity. We contribute a multi-modal dataset of 3 events (12~UGVs), which is used to validate the proposed gyro-based video composition framework.El incremento de la disponibilidad de teléfonos inteligentes o smartphones posibilita a la gente capturar videos de sus experiencias cuando asisten a eventos así como como conciertos, competiciones deportivas o mítines públicos. Los Videos Generados por Usuarios (UGVs) pueden estar disponibles en sitios web públicos especializados en compartir archivos. La búsqueda y la minería de datos de los UGVs del mismo evento son un reto debido a que los etiquetajes son incoherentes o las marcas de tiempo erróneas. Por otra parte, un UGV grabado desde una ubicación fija, contiene información monótona y movimientos de cámara no intencionados haciendo menos interesante su reproducción. En esta tesis, se propone una identificación, sincronización y composición de tramas de vídeo para UGVs. Se ha propuesto un sistema para la identificación y sincronización automática de UGVs no editados provenientes de diferentes cámaras dentro de una base de datos. El sistema propuesto analiza el sonido con el fin de hacerlo coincidir e integrar UGVs que capturan el mismo evento en el espacio y en el tiempo, estimando sus respectivos desfases temporales y alinearlos en el tiempo. Se ha diseñado un nuevo descriptor a partir de la coincidencia por parejas de características de la croma del audio de los UGVs. Este descriptor facilita la determinación de una clasificación por umbral para una identificación de eventos automática basada en búsqueda mediante ejemplo (en inglés, query by example). Se ha contribuido con una base de datos de 263 multi-cámaras UGVs de un total de 48 eventos reales. Se ha evaluado la trama propuesta en esta base de datos y se ha comparado con los métodos elaborados en el estado del arte. Los resultados experimentales muestran la efectividad del enfoque propuesto con la presencia alteraciones en el audio. Además, se ha presentado una selección automática de tramas en base a la reproducción de video y audio componiendo una grabación ininterrumpida de multi-cámaras UGVs sincronizadas en el mismo evento. También se ha diseñado un método de selección de puntos de corte automático basado en audio que proporciona una referencia común para la segmentación de audio y video. Con el fin de filtrar segmentos de videos de baja calidad, se han calculado algunas medidas espaciales y espacio-temporales. El sistema combina segmentos de UGVs empleando una estrategia de selección de cámaras basadas en la evaluación a través de un ranking considerando puntuaciones de calidad visuales y diversidad de visión. El sistema propuesto se ha validado con un conjunto de datos de 13 eventos (93 UGVs) a través de pruebas subjetivas y se han comparado con los métodos elaborados en el estado del arte. La selección de puntos de corte adecuados, evaluaciones de calidad visual específicas y la selección de cámara basada en ranking contribuyen en la mejoría de calidad del sistema propuesto respecto a otros métodos existentes. Finalmente, se ha realizado un método para la Detección de Movimiento de Cámara usando giróscopos para las UGVs capturadas desde smartphones y se ha diseñado un método de puntuación de calidad basada en el giro. El método de detección de movimiento de la cámara con una base de datos de 24 UGVs multi-modales y se ha comparado con los métodos actuales basados en visión y sistemas inerciales. A través del diseño de puntuación para cuantificar con el giróscopo cuán bien funcionan los sistemas de UGVs con multi-cámara, se ha desarrollado un sistema de composición de video basada en el movimiento del giroscopio. Este sistema basado en la puntuación a través del giróscopo sustituye a los sistemas de puntuaciones basados en parámetros espacio-temporales reduciendo la complejidad computacional. Además, se ha contribuido con un conjunto de datos de 3 eventos (12 UGVs), que se han empleado para validar los sistemas de composición de video basados en giróscopo.Postprint (published version

Cognitive Robots for Social Interactions

One of my goals is to work towards developing Cognitive Robots, especially with regard to improving the functionalities that facilitate the interaction with human beings and their surrounding objects. Any cognitive system designated for serving human beings must be capable of processing the social signals and eventually enable efficient prediction and planning of appropriate responses. My main focus during my PhD study is to bridge the gap between the motoric space and the visual space. The discovery of the mirror neurons ([RC04]) shows that the visual perception of human motion (visual space) is directly associated to the motor control of the human body (motor space). This discovery poses a large number of challenges in different fields such as computer vision, robotics and neuroscience. One of the fundamental challenges is the understanding of the mapping between 2D visual space and 3D motoric control, and further developing building blocks (primitives) of human motion in the visual space as well as in the motor space. First, I present my study on the visual-motoric mapping of human actions. This study aims at mapping human actions in 2D videos to 3D skeletal representation. Second, I present an automatic algorithm to decompose motion capture (MoCap) sequences into synergies along with the times at which they are executed (or "activated") for each joint. Third, I proposed to use the Granger Causality as a tool to study the coordinated actions performed by at least two units. Recent scientific studies suggest that the above "action mirroring circuit" might be tuned to action coordination rather than single action mirroring. Fourth, I present the extraction of key poses in visual space. These key poses facilitate the further study of the "action mirroring circuit". I conclude the dissertation by describing the future of cognitive robotics study

Discriminative Feature Learning with Foreground Attention for Person Re-Identification

The performance of person re-identification (Re-ID) has been seriously effected by the large cross-view appearance variations caused by mutual occlusions and background clutters. Hence learning a feature representation that can adaptively emphasize the foreground persons becomes very critical to solve the person Re-ID problem. In this paper, we propose a simple yet effective foreground attentive neural network (FANN) to learn a discriminative feature representation for person Re-ID, which can adaptively enhance the positive side of foreground and weaken the negative side of background. Specifically, a novel foreground attentive subnetwork is designed to drive the network's attention, in which a decoder network is used to reconstruct the binary mask by using a novel local regression loss function, and an encoder network is regularized by the decoder network to focus its attention on the foreground persons. The resulting feature maps of encoder network are further fed into the body part subnetwork and feature fusion subnetwork to learn discriminative features. Besides, a novel symmetric triplet loss function is introduced to supervise feature learning, in which the intra-class distance is minimized and the inter-class distance is maximized in each triplet unit, simultaneously. Training our FANN in a multi-task learning framework, a discriminative feature representation can be learned to find out the matched reference to each probe among various candidates in the gallery. Extensive experimental results on several public benchmark datasets are evaluated, which have shown clear improvements of our method over the state-of-the-art approaches