Event detection in field sports video using audio-visual features and a support vector machine

In this paper, we propose a novel audio-visual feature-based framework for event detection in broadcast video of multiple different field sports. Features indicating significant events are selected and robust detectors built. These features are rooted in characteristics common to all genres of field sports. The evidence gathered by the feature detectors is combined by means of a support vector machine, which infers the occurrence of an event based on a model generated during a training phase. The system is tested generically across multiple genres of field sports including soccer, rugby, hockey, and Gaelic football and the results suggest that high event retrieval and content rejection statistics are achievable

Parking lot monitoring system using an autonomous quadrotor UAV

The main goal of this thesis is to develop a drone-based parking lot monitoring system using low-cost hardware and open-source software. Similar to wall-mounted surveillance cameras, a drone-based system can monitor parking lots without affecting the flow of traffic while also offering the mobility of patrol vehicles. The Parrot AR Drone 2.0 is the quadrotor drone used in this work due to its modularity and cost efficiency. Video and navigation data (including GPS) are communicated to a host computer using a Wi-Fi connection. The host computer analyzes navigation data using a custom flight control loop to determine control commands to be sent to the drone. A new license plate recognition pipeline is used to identify license plates of vehicles from video received from the drone

A Graph-Based Method for Soccer Action Spotting Using Unsupervised Player Classification

Action spotting in soccer videos is the task of identifying the specific time when a certain key action of the game occurs. Lately, it has received a large amount of attention and powerful methods have been introduced. Action spotting involves understanding the dynamics of the game, the complexity of events, and the variation of video sequences. Most approaches have focused on the latter, given that their models exploit the global visual features of the sequences. In this work, we focus on the former by (a) identifying and representing the players, referees, and goalkeepers as nodes in a graph, and by (b) modeling their temporal interactions as sequences of graphs. For the player identification, or player classification task, we obtain an accuracy of 97.72% in our annotated benchmark. For the action spotting task, our method obtains an overall performance of 57.83% average-mAP by combining it with other audiovisual modalities. This performance surpasses similar graph-based methods and has competitive results with heavy computing methods. Code and data are available at https://github.com/IPCV/soccer_action_spotting.Comment: Accepted at the 5th International ACM Workshop on Multimedia Content Analysis in Sports (MMSports 2022

An Efficient Boosted Classifier Tree-Based Feature Point Tracking System for Facial Expression Analysis

The study of facial movement and expression has been a prominent area of research since the early work of Charles Darwin. The Facial Action Coding System (FACS), developed by Paul Ekman, introduced the first universal method of coding and measuring facial movement. Human-Computer Interaction seeks to make human interaction with computer systems more effective, easier, safer, and more seamless. Facial expression recognition can be broken down into three distinctive subsections: Facial Feature Localization, Facial Action Recognition, and Facial Expression Classification. The first and most important stage in any facial expression analysis system is the localization of key facial features. Localization must be accurate and efficient to ensure reliable tracking and leave time for computation and comparisons to learned facial models while maintaining real-time performance. Two possible methods for localizing facial features are discussed in this dissertation. The Active Appearance Model is a statistical model describing an object\u27s parameters through the use of both shape and texture models, resulting in appearance. Statistical model-based training for object recognition takes multiple instances of the object class of interest, or positive samples, and multiple negative samples, i.e., images that do not contain objects of interest. Viola and Jones present a highly robust real-time face detection system, and a statistically boosted attentional detection cascade composed of many weak feature detectors. A basic algorithm for the elimination of unnecessary sub-frames while using Viola-Jones face detection is presented to further reduce image search time. A real-time emotion detection system is presented which is capable of identifying seven affective states (agreeing, concentrating, disagreeing, interested, thinking, unsure, and angry) from a near-infrared video stream. The Active Appearance Model is used to place 23 landmark points around key areas of the eyes, brows, and mouth. A prioritized binary decision tree then detects, based on the actions of these key points, if one of the seven emotional states occurs as frames pass. The completed system runs accurately and achieves a real-time frame rate of approximately 36 frames per second. A novel facial feature localization technique utilizing a nested cascade classifier tree is proposed. A coarse-to-fine search is performed in which the regions of interest are defined by the response of Haar-like features comprising the cascade classifiers. The individual responses of the Haar-like features are also used to activate finer-level searches. A specially cropped training set derived from the Cohn-Kanade AU-Coded database is also developed and tested. Extensions of this research include further testing to verify the novel facial feature localization technique presented for a full 26-point face model, and implementation of a real-time intensity sensitive automated Facial Action Coding System

Knowledge Extraction in Video Through the Interaction Analysis of Activities

Video is a massive amount of data that contains complex interactions between moving objects. The extraction of knowledge from this type of information creates a demand for video analytics systems that uncover statistical relationships between activities and learn the correspondence between content and labels. However, those are open research problems that have high complexity when multiple actors simultaneously perform activities, videos contain noise, and streaming scenarios are considered. The techniques introduced in this dissertation provide a basis for analyzing video. The primary contributions of this research consist of providing new algorithms for the efficient search of activities in video, scene understanding based on interactions between activities, and the predicting of labels for new scenes

Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer\u27s Disease, Brain Tumors, to Assisted Living

Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer\u27s disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. For Alzheimer’s disease (AD) diagnosis, apart from symptoms of patients, Magnetic Resonance Images (MRIs) also play an important role. Inspired by the success of deep learning, a new multi-stream multi-scale Convolutional Neural Network (CNN) architecture is proposed for AD detection from MRIs, where AD features are characterized in both the tissue level and the scale level for improved feature learning. Good classification performance is obtained for AD/NC (normal control) classification with test accuracy 94.74%. In glioma subtype classification, biopsies are usually needed for determining different molecular-based glioma subtypes. We investigate non-invasive glioma subtype prediction from MRIs by using deep learning. A 2D multi-stream CNN architecture is used to learn the features of gliomas from multi-modal MRIs, where the training dataset is enlarged with synthetic brain MRIs generated by pairwise Generative Adversarial Networks (GANs). Test accuracy 88.82% has been achieved for IDH mutation (a molecular-based subtype) prediction. A new deep semi-supervised learning method is also proposed to tackle the problem of missing molecular-related labels in training datasets for improving the performance of glioma classification. In other two applications, we also address video-based human fall detection by using co-saliency-enhanced Recurrent Convolutional Networks (RCNs), as well as obstacle avoidance in prosthetic vision by characterizing obstacle-related video features using a Spiking Neural Network (SNN). These investigations can benefit future research, where artificial intelligence/deep learning may open a new way for real medical applications

Multi-Sensory Emotion Recognition with Speech and Facial Expression

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering. The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. Emotion recognition can be done from many sources including text, speech, hand, and body gesture as well as facial expression. Presently, most of the emotion recognition methods only use one of these sources. The emotion of human beings changes every second and using a single way to process the emotion recognition may not reflect the emotion correctly. This research is motivated by the desire to understand and evaluate human beings’ emotion from multiple ways such as speech and facial expressions. In this dissertation, multi-sensory emotion recognition has been exploited. The proposed framework can recognize emotion from speech, facial expression, and both of them. There are three important parts in the design of the system: the facial emotion recognizer, the speech emotion recognizer, and the information fusion. The information fusion part uses the results from the speech emotion recognition and facial emotion recognition. Then, a novel weighted method is used to integrate the results, and a final decision of the emotion is given after the fusion. The experiments show that with the weighted fusion methods, the accuracy can be improved to an average of 3.66% compared to fusion without adding weight. The improvement of the recognition rate can reach 18.27% and 5.66% compared to the speech emotion recognition and facial expression recognition, respectively. By improving the emotion recognition accuracy, the proposed multi-sensory emotion recognition system can help to improve the naturalness of human computer interaction

Multi-Sensory Emotion Recognition with Speech and Facial Expression

Emotion plays an important role in human beings’ daily lives. Understanding emotions and recognizing how to react to others’ feelings are fundamental to engaging in successful social interactions. Currently, emotion recognition is not only significant in human beings’ daily lives, but also a hot topic in academic research, as new techniques such as emotion recognition from speech context inspires us as to how emotions are related to the content we are uttering. The demand and importance of emotion recognition have highly increased in many applications in recent years, such as video games, human computer interaction, cognitive computing, and affective computing. Emotion recognition can be done from many sources including text, speech, hand, and body gesture as well as facial expression. Presently, most of the emotion recognition methods only use one of these sources. The emotion of human beings changes every second and using a single way to process the emotion recognition may not reflect the emotion correctly. This research is motivated by the desire to understand and evaluate human beings’ emotion from multiple ways such as speech and facial expressions. In this dissertation, multi-sensory emotion recognition has been exploited. The proposed framework can recognize emotion from speech, facial expression, and both of them. There are three important parts in the design of the system: the facial emotion recognizer, the speech emotion recognizer, and the information fusion. The information fusion part uses the results from the speech emotion recognition and facial emotion recognition. Then, a novel weighted method is used to integrate the results, and a final decision of the emotion is given after the fusion. The experiments show that with the weighted fusion methods, the accuracy can be improved to an average of 3.66% compared to fusion without adding weight. The improvement of the recognition rate can reach 18.27% and 5.66% compared to the speech emotion recognition and facial expression recognition, respectively. By improving the emotion recognition accuracy, the proposed multi-sensory emotion recognition system can help to improve the naturalness of human computer interaction

Abordando la medición automática de la experiencia de la audiencia en línea

Trabajo de Fin de Grado del Doble Grado en Ingeniería Informática y Matemáticas, Facultad de Informática UCM, Departamento de Ingeniería del Software e Inteligencia Artificial, Curso 2020/2021The availability of automatic and personalized feedback is a large advantage when facing an audience. An effective way to give such feedback is to analyze the audience experience, which provides valuable information about the quality of a speech or performance. In this document, we present the design and implementation of a computer vision system to automatically measure audience experience. This includes the definition of a theoretical and practical framework grounded on the theatrical perspective to quantify this concept, the development of an artificial intelligence system which serves as a proof-of-concept of our approach, and the creation of a dataset to train our system. To facilitate the data collection step, we have also created a custom video conferencing tool. Additionally, we present the evaluation of our artificial intelligence system and the final conclusions.La disponibilidad de feedback automático y personalizado supone una gran ventaja a la hora de enfrentarse a un público. Una forma efectiva de dar este tipo de feedback es analizar la experiencia de la audiencia, que proporciona información fundamental sobre la calidad de una ponencia o actuación. En este documento exponemos el diseño e implementación de un sistema automático de medición de la experiencia de la audiencia basado en la visión por computador. Esto incluye la definición de un marco teórico y práctico fundamentado en la perspectiva del mundo del teatro para cuantificar el concepto de experiencia de la audiencia, el desarrollo de un sistema basado en inteligencia artificial que sirve como prototipo de nuestra aproximación y la recopilación un conjunto de datos para entrenar el sistema. Para facilitar este último paso hemos desarrolado una aplicación de videoconferencias personalizada. Además, en este trabajo presentamos la evaluación de nuestro sistema de inteligencia artificial y las conclusiones extraídas.Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEunpu