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

A Link Control Scheme Using Self-Reflecting Signal in Global Beam Satellite Network

In this letter, we show that we can improve the existing Go-back-N automatic repeat request (ARQ) in satellite communication by using a self-reflecting signal from the satellite. We also provide the analyses of the existing Go-back-N ARQ and our scheme. In our scheme, we use a self-reflecting signal from the satellite in order to investigate whether the transmitting frame experiences errors or not. Identification of the next arriving frame is also performed during the timeout event. Finally, through the numerical results, we can find the better performance of proposing link control scheme

Developing On-Road NOx Emission Factors for Euro 6b Light-Duty Diesel Trucks in Korean Driving Conditions

This study aimed to develop on-road NOx emission factors for Euro 6b light-duty diesel trucks (LDDTs) in Korea. On-road NOx emissions were measured using portable emissions measurement systems and compared with those measured using the Korean Driving Cycle (KDC), the conventional laboratory test used to develop emission factors. To ensure the representativeness of the LDDTs emission factors, five vehicles of three models were driven along two real driving routes for total traveled mileage of 2280 km. On-road NOx levels were 2.1 to 6.9 times higher on average than those measured using the KDC because the latter does not cover the wide variability in vehicle speed and relative positive acceleration, common in real driving conditions. The lean-NOx trap was found to have disappointingly low NOx reduction efficiency in on-road driving. The on-road NOx emission factors by vehicle speeds developed in this study were comparable to the COPERT 4 factors

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