to groundbreaking changes. The data are from sources such as tracking position and motion of athletes in basketball ( Thomas, Gade, Moeslund, Carr, & Hilton, 2017 ) and baseball and football match statistics ( Stensland et al., 2014 ). Furthermore, new hardware platforms appear, such as LED displays
Search Results
Towards Automatic Modeling of Volleyball Players’ Behavior for Analysis, Feedback, and Hybrid Training
Fahim A. Salim, Fasih Haider, Dees Postma, Robby van Delden, Dennis Reidsma, Saturnino Luz, and Bert-Jan van Beijnum
High-Tech Video Capture and Analysis for Counting Park Users
Richard R. Suminski, Gregory M. Dominick, Philip Saponaro, Elizabeth M. Orsega-Smith, Eric Plautz, and Matthew Saponaro
.g., basketball court, baseball field) and equipment (e.g., swings, slides, climbing equipment) typically used for physical activity. We focused on neighborhood parks because they are the most common park type and serve a considerable proportion of the population as compared to mini parks (< 1 acre in size) and larger
Comparing Counts of Park Users With a Wearable Video Device and an Unmanned Aerial System
Richard R. Suminski, Gregory M. Dominick, and Matthew Saponaro
.3); 12 0.9 (1.3); 12 Baseball (two videos/device) 0.5 (0.7); 1 0.5 (0.7); 1 Horseshoe pit (two videos/device) 0 0 Totals (86 videos/device) 4.5 (9.0); 385 4.7 (9.6); 404 Note . WVD = wearable video device; UAS = unmanned aerial systems. Absolute agreement between WVD and UAS counts was 86% (74/86), with
