Pengenalan Ekspresi Wajah Untuk Tunanetra Menggunakan Deep Learning Pada Perangkat Portabel

Penyandang tunanetra menghadapi tantangan yang lebih dalam kehidupan sehari-hari seperti mencari jalan di lingkungan yang tidak dikenal, mendeteksi objek dan orang, dan pengenalan wajah dan ekspresi wajah. Salah satunya adalah interaksi tatap muka yang dimana banyak informasi dipertukarkan secara nonverbal melalui postur tubuh, gerakan, kedekatan interpersonal dan ekspresi wajah. Ekspresi wajah dipercayai mempunyai kaitan erat dalam emosi seseorang dan memberikan informasi yang lebih lengkap saat berkomunikasi. Penyandang tunanetra tidak bisa mendapatkan informasi tersebut sehingga adanya batasan dalam interaksi tatap muka. Penelitian tentang pengenalan ekspresi wajah kepada penyandang tunanetra telah dilakukan. Akan tetapi, kendala yang selalu dihadapi adalah biaya pembuatan dan desain perangkat yang kurang praktis. Pada penelitian ini akan dibuat sebuah perangkat yang lebih praktis dan lebih murah dengan performa yang baik. Dengan begitu, akan lebih banyak penyandang tunanetra yang terbantu. Pada penelitian ini dibuat perangkat pembantu pengenalan ekspresi wajah untuk tunanetra yang akan diimplementasikan ke perangkat yang relatif kecil yaitu raspberry pi. Perangkat akan menyampaikan informasi ke pengguna melalui suara. Deep learning menjadi bagian utama dalam pengenalan ekspresi wajah. Model deep learning akan dibuat menggunakan gabungan dataset yang sudah ada dan juga memanfaatkan pre-trained network. Perangkat yang didesain pada penelitian ini mempunyai akurasi mencapai 80% pada ruangan indoor dan 75% pada ruangan outdoor. ======================================================== Persons with visual impairments have more hurdle than normal person in daily life such as finding roads in unknown environments, detecting object and people, facial recognition and expressions. One of it is face-to-face interaction that involved non-verbal information through body gesture, interpersonal connection, and facial expressions. Facial expression believed have a strong bond in human emotion and gives more complete informations when communicate. Visually impaired persons people cannot have that information so they have hinderance while communication in face-to-face interraction. There are reasearch about delivering facial expressions to visually impaired persons. But, the problem always about production cost and uncomfortable design. In this research we will make less cost device and comfortable with good performance. In this study, system to recognize facial expressions for blind people was made which would be implemented on a relatively small device, raspberry pi. The device will convey information to the user via voice. Deep learning is a major part of the facial expressions recognition system. The deep learning model will be created using a combination of existing datasets and make use of pre-trained network. Devices designed in this study have an accuracy of up to 80% in indoor and 75% in outdoor spaces

SoccerNet 2023 Challenges Results

peer reviewedThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet

SoccerNet 2023 challenges results

SoccerNet 2023 Challenges ResultsThe SoccerNet 2023 challenges were the third annual video understanding challenges organized by the SoccerNet team. For this third edition, the challenges were composed of seven vision-based tasks split into three main themes. The first theme, broadcast video understanding, is composed of three high-level tasks related to describing events occurring in the video broadcasts: (1) action spotting, focusing on retrieving all timestamps related to global actions in soccer, (2) ball action spotting, focusing on retrieving all timestamps related to the soccer ball change of state, and (3) dense video captioning, focusing on describing the broadcast with natural language and anchored timestamps. The second theme, field understanding, relates to the single task of (4) camera calibration, focusing on retrieving the intrinsic and extrinsic camera parameters from images. The third and last theme, player understanding, is composed of three low-level tasks related to extracting information about the players: (5) re-identification, focusing on retrieving the same players across multiple views, (6) multiple object tracking, focusing on tracking players and the ball through unedited video streams, and (7) jersey number recognition, focusing on recognizing the jersey number of players from tracklets. Compared to the previous editions of the SoccerNet challenges, tasks (2-3-7) are novel, including new annotations and data, task (4) was enhanced with more data and annotations, and task (6) now focuses on end-to-end approaches. More information on the tasks, challenges, and leaderboards are available on https://www.soccer-net.org. Baselines and development kits can be found on https://github.com/SoccerNet