Automatic tracking of indoor sports players using a video-based deep learning approach: a concurrent validity study - Giovanini, B., Moura, F. A. (Sports Engineering, 2026)
This study aimed to assess the concurrent validity of DeepLabCut to estimate a player`s position and velocity in a pre-delimited course in an indoor environment. Ten young male basketball players (age: 16.5 ± 0.5 years; height: 181.7 ± 2.4 cm; body mass: 75.7 ± 3.6 Kg) were submitted to static and dynamic tasks. The proposed tracking method with a single camera was compared to an optoelectronic system. Validity was analyzed by means of absolute and relative errors, Bland-Altman plot analyses, intraclass correlation coefficients, root mean square errors, and statistical parametric mapping for velocity time series. Overall, intraclass correlation coefficients values showed good to excellent (0.78-0.94) reliability between systems. Errors were higher as the proposed speed of the task increased. Statistical parametric mapping indicated significant differences (p < 0.05) in velocity curves of both systems during moments of 90-degree change of direction. However, no significant differences were found regarding linear displacements or on change-of-direction instants at lower velocities. Collectively, our results showed that DeepLabCut can be considered a valid and cost-effective method for determining the distance traveled and the instantaneous velocity during on-court activities, making high-quality tracking accessible to organizations with limited resources.