A Study of Method in Facial Emotional Recognitation

Facial expressions make important role in social communication and widely used in the behavioral interpretation of emotions. Automatic facial expression recognition is one of the most provocative and stimulate obstacle in computer vision due to its potential utilization such as Human Computer Interaction (HCI), behavioral science, video games etc. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Even though there is lots of research using static images, the research is still going on for the development of new methods which would be quiet easy in computation and would have less memory usage as compared to previous methods. This paper presents a quick compare of facial expression recognition. A comparative study of various feature extraction techniques by different method

Reconnaissance d'objets multiclasses pour des applications d'aide à la conduite et de vidéo surveillance

Co-encadrement de la thèse : Bogdan StanciulescuPedestrian Detection and Traffic Sign Recognition (TSR) are important components of an Advanced Driver Assistance System (ADAS). This thesis presents two methods for eliminating false alarms in pedestrian detection applications and a novel three stage approach for TSR. Our TSR approch consists of a color segmentation, a shape detection and a content classification phase. The red color enhancement is improved by using an adaptive threshold. The performance of the K-d tree is augmented by introducing a spatial weighting. The Random Forests yield a classification accuracy of 97% on the German Traffic Sign Recognition Benchmark. Moreover, the processing and memory requirements are reduced by employing a feature space reduction. The classifiers attain an equally high classification rate using only a fraction of the feature dimension, selected using the Random Forest or Fisher's Criterion. This technique is also validated on two different multiclass benchmarks: ETH80 and Caltech 101. Further, in a static camera video surveillance application, the immobile false positives, such as trees and poles, are eliminated using the correlation measure over several frames. The recurring false alarms in the pedestrian detection in the scope of an embedded ADAS application are removed using a complementary tree filter.La détection de piétons et la reconnaissance des panneaux routiers sont des fonctions importantes des systèmes d'aide à la conduite (anglais : Advanced Driver Assistance System - ADAS). Une nouvelle approche pour la reconnaissance des panneaux et deux méthodes d'élimination de fausses alarmes dans des applications de détection de piétons sont présentées dans cette thèse. Notre approche de reconnaissance de panneaux consiste en trois phases: une segmentation de couleurs, une détection de formes et une classification du contenu. Le color enhancement des régions rouges est amélioré en introduisant un seuil adaptatif. Dans la phase de classification, la performance du K-d tree est augmentée en utilisant un poids spatial. Les Random Forests obtiennent un taux de classification de 97% sur le benchmark allemand de la reconnaissance des panneaux routiers (German Traffic Sign Recognition Benchmark). Les besoins en mémoire et calcul sont réduits en employant une réduction de la dimension des caractéristiques. Les classifieurs atteignent un taux de classification aussi haut qu'avec une fraction de la dimension des caractéristiques, selectionée en utilisant des Random Forests ou Fisher's Crtierion. Cette technique est validée sur deux benchmarks d'images multiclasses : ETH80 et Caltech 101. Dans une application de vidéo surveillance avec des caméras statiques, les fausses alarmes des objets fixes, comme les arbres et les lampadaires, sont éliminées avec la corrélation sur plusieurs trames. Les fausses alarmes récurrentes sont supprimées par un filtre complémentaire en forme d'arbre

Overview of Environment Perception for Intelligent Vehicles

This paper presents a comprehensive literature review on environment perception for intelligent vehicles. The state-of-the-art algorithms and modeling methods for intelligent vehicles are given, with a summary of their pros and cons. A special attention is paid to methods for lane and road detection, traffic sign recognition, vehicle tracking, behavior analysis, and scene understanding. In addition, we provide information about datasets, common performance analysis, and perspectives on future research directions in this area

Improving Search through A3C Reinforcement Learning based Conversational Agent

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.Comment: 17 pages, 7 figure

Improved detection techniques in autonomous vehicles for increased road safety

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2020.A futura adoção em massa de Veículos Autônomos traz um potencial significativo para aumentar a segurança no trânsito para ambos os motoristas e pedestres. Como reportado pelo Departamento de Transportes dos E.U.A., cerca de 94% dos acidentes de trânsito são causados por erro humano. Com essa realidade em mente, a indústria automotiva e pesquisadores acadêmicos ambicionam alcançar direção totalmente automatizada em cenários reais nos próximos anos. Para tal, algorit- mos mais precisos e sofisticados são necessários para que os veículos autônomos possam tomar decisões corretas no tráfego. Nesse trabalho, é proposta uma técnica melhorada de detecção de pedestres, com um aumento de precisão de até 31% em relação aos benchmarks atuais. Em seguida, de forma a acomodar a infraestrutura de trânsito já existente, avançamos a precisão na detecção de placas de trânsito com base em Redes Neurais Convolucionais. Nossa abordagem melhora substancialmente a acurácia em relação ao modelo-base considerado. Finalmente, ap- resentamos uma proposta de fusão de dados precoce, a qual mostramos surpassar abordagens de detecção com um só sensor e fusão de dados tardia em até 20%.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES).The future widespread use of Autonomous Vehicles has a significant potential to increase road safety for drivers and pedestrians alike. As reported by the U.S. Department of Transportation, up to 94% of transit accidents are caused by human error. With that reality in mind, the auto- motive industry and academic researches are striving to achieve fully automated driving in real scenarios in the upcoming years. For that, more sophisticated and precise detection algorithms are necessary to enable the autonomous vehicles to take correct decisions in transit. This work proposes an improved technique for pedestrian detection that increases precision up to 31% over current benchmarks. Next, in order to accommodate current traffic infrastructure, we enhance performance of a traffic sign recognition algorithm based on Convolutional Neural Networks. Our approach substantially raises precision of the base model considered. Finally, we present a proposal for early data fusion of camera and LiDAR data, which we show to surpass detection using individual sensors and late fusion by up to 20%

Enhancing Deep Neural Networks Testing by Traversing Data Manifold

We develop DEEPTRAVERSAL, a feedback-driven framework to test DNNs. DEEPTRAVERSAL first launches an offline phase to map media data of various forms to manifolds. Then, in its online testing phase, DEEPTRAVERSAL traverses the prepared manifold space to maximize DNN coverage criteria and trigger prediction errors. In our evaluation, DNNs executing various tasks (e.g., classification, self-driving, machine translation) and media data of different types (image, audio, text) were used. DEEPTRAVERSAL exhibits better performance than prior methods with respect to popular DNN coverage criteria and it can discover a larger number and higher quality of error-triggering inputs. The tested DNN models, after being repaired with findings of DEEPTRAVERSAL, achieve better accurac