TY - GEN
T1 - 3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy
AU - Yang, Yukun
AU - Xu, Min
AU - Wu, Wanneng
AU - Zhang, Ruiheng
AU - Peng, Yu
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What's more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.
AB - This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What's more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.
KW - Multiview
KW - Object Detection
KW - Pedestrian Localization
UR - http://www.scopus.com/inward/record.url?scp=85062220381&partnerID=8YFLogxK
U2 - 10.1109/DICTA.2018.8615798
DO - 10.1109/DICTA.2018.8615798
M3 - Conference contribution
AN - SCOPUS:85062220381
T3 - 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
BT - 2018 International Conference on Digital Image Computing
A2 - Pickering, Mark
A2 - Zheng, Lihong
A2 - You, Shaodi
A2 - Rahman, Ashfaqur
A2 - Murshed, Manzur
A2 - Asikuzzaman, Md
A2 - Natu, Ambarish
A2 - Robles-Kelly, Antonio
A2 - Paul, Manoranjan
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018
Y2 - 10 December 2018 through 13 December 2018
ER -