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Prezados colegas,   Solicito gentilmente a vossa colaboração para ampla divulgação desta oportunidade de bolsa FAPESP, conforme abaixo:   Fomento Instituto Virtual de Pesquisas FAPESP - Microsoft Research (2017/25908-6)   Projeto Aprendizado Fracamente Supervisionado para Análise de Vídeos no Domínio Comprimido em Tarefas de Recuperação e Classificação para Alertas Visuais    Link do Projeto https://bv.fapesp.br/en/auxilios/102700/weakly-supervised-learning-for-compressed-video-analysis-on-retrieval-and-classification-tasks-for-v/   Objetivo O objetivo dessa bolsa é ter um(a) desenvolvedor(a) com uma boa experiência em programação para participar do desenvolvimento de uma ferramenta para análise e representação de vídeo usando aprendizagem profunda. Ele(a) irá trabalhar em tarefas de apoio à condução e análise dos experimentos que serão executados como parte do projeto, e espera-se sua participação na escrita de artigos científicos.   Pe...

In this paper, entitled "Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain", we present a deep neural network for human action recognition able to learn straight from compressed video. Our network is a two-stream CNN integrating both frequency (i.e., transform coefficients) and temporal (i.e., motion vectors) information, which can be extracted by parsing and entropy decoding the stream of encoded video data.  The starting point for our proposal is the CoViAR [1] approach. In essence, CoViAR extends TSN [2] to exploit three information available in MPEG-4 compressed streams: (1) RGB images encoded in I-frames , (2) motion vectors, and (3) residuals encoded in P-frames. Although CoViAR has been designed to operate with video data in the compressed domain, it still demands a preliminary decoding step, since the frequency domain representation (i.e., DCT coefficients) used to encode the pictures in I-frames and the residuals in P-frames needs to be decoded to the sp...

This paper proposes an adaptive steering control strategy for self-driving cars based on a Fuzzy Expert System and Reinforcement Learning. Our objective consists in deriving an appropriate control law directly from a real vehicle that allows it to navigate on several types of lanes, by controlling the position in relation to the center of the tracks and also the translation speed of the vehicle. Using an on-line Reinforcement Learning approach, the Fuzzy expert controller is derived considering the coupling and non-linearity of the model on straight and winding tracks. To do this, an embedded camera captures the images and sends them to the computer vision algorithm responsible for performing tracks detection and recognition. From that, the control references which indicate the navigation path and direction on the lane are calculated. The main contribution of this work is to apply an online reinforcement learning approach to tune and optimize the fuzzy steering controller while the vehicle navigates throug...