• Advanced Institute of Engineering and Technology (AVITECH)

  • Seminars

    November 19, 2019: Dr. Nguyen Hong Thinh (AVITECH), A video-based tracking system for football players analysis using Efficient Convolution Operators

    Multiple Objects Tracking (MOT) is a challenging problem of computer vision that has a wide range of practical applications in CCTV, security, video compression, and sports analysis. Generally, MOT tracking contains multiple single tracking operating at the same time. It becomes very difficult in cases demanding realtime processing with high accuracy. In such a context, exploiting frequency domain with multiple features such as color, shape, and deep-features has been proposed by many authors to improve both accuracy and performance. This tutorial is addressed to the audience with a general background in tracking problems and introduce a practical application in the tracking of multiple football players.

    Speaker: Dr. Nguyen Hong Thinh, AVITECH

    Time: 15:30, Tuesday, November 19, 2019

    Venue: G2-315, 144 Xuan Thuy, Cau Giay, Hanoi

    speaker

    Dr. Nguyen Hong Thinh received B.Sc. degree in Electronics and Telecommunications from the University of Engineering and Technology (UET) 2007, Master degree in Information Systems and Technology, Paris Sud 11 (Poles Universitaire Francais programme), 2010, and the Ph.D. degree in computer vision, University Saint-Etienne, University Lyon1 2014. She is now a teacher and researcher of the Signal and System laboratory within VNU-UET. Her research interests include image processing, machine learning, and computer vision.

    SAME CATEGORY

    February 2, 2024: Dr. Khoa D. Doan (Vin Univeristy) Toward Reliable and Practical Machine Learning Applications

    While Machine Learning (ML) has rapidly transformed several domains and applications with incredible successes, there are also important areas where the progress is significantly slower. Specifically, there exists a widened complexity gap between the methods currently investigated in research and those used in practice in these areas. One reason is that many algorithms, despite achieving […]

    February 2, 2024: Prof. Heng Ji (University of Illinois at Urbana-Champaign), Combating with Misinformation and Cancer: A Unified Multimodal AI Approach to Healthy and Happy Life

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    January 11, 2024: Dr. Le Duc Trong (FIT-UET) Resilient Multimodal Learning for Multimodal Emotion Recognition in the Presence of Incomplete Modalities

    Multimodal Emotion Recognition in Conversation (Multimodal ERC) is a critical area of research for interpreting human communication in diverse applications. Nevertheless, the persistent issue of uncertain missing modalities poses a major hurdle, hampering the development of robust Multimodal ERC models. Existing approaches face limitations in effectively leveraging a fusion of diverse data modalities encompassing audio, […]