no code implementations • 22 May 2022 • Kanav Vats, Mehrnaz Fani, David A. Clausi, John S. Zelek
Tracking and identifying players is an important problem in computer vision based ice hockey analytics.
no code implementations • 22 Nov 2021 • Kanav Vats, William McNally, Pascale Walters, David A. Clausi, John S. Zelek
Obtaining player identities is essential for analyzing the game and is used in downstream tasks such as game event recognition.
Optical Character Recognition Optical Character Recognition (OCR) +3
1 code implementation • 16 Nov 2021 • William McNally, Kanav Vats, Alexander Wong, John McPhee
In keypoint estimation tasks such as human pose estimation, heatmap-based regression is the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer from quantization error and require excessive computation to generate and post-process.
Ranked #9 on Pose Estimation on CrowdPose
no code implementations • 6 Oct 2021 • Kanav Vats, Pascale Walters, Mehrnaz Fani, David A. Clausi, John Zelek
The player identification model further takes advantage of the available NHL game roster data to obtain a player identification accuracy of 83%.
no code implementations • 17 Aug 2021 • Kanav Vats, Mehrnaz Fani, David A. Clausi, John Zelek
Identifying players in sports videos by recognizing their jersey numbers is a challenging task in computer vision.
no code implementations • 21 May 2021 • Kanav Vats, Mehrnaz Fani, David A. Clausi, John Zelek
In this paper, we introduce and implement a network for puck localization in broadcast hockey video.
1 code implementation • 20 May 2021 • William McNally, Pascale Walters, Kanav Vats, Alexander Wong, John McPhee
In the primary dataset containing 15k images captured from a face-on view of the dartboard using a smartphone, DeepDarts predicted the total score correctly in 94. 7% of the test images.
1 code implementation • 17 Nov 2020 • William McNally, Kanav Vats, Alexander Wong, John McPhee
Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks.
Ranked #1 on Multi-Person Pose Estimation on MS COCO (Validation AP metric)
no code implementations • 13 Apr 2020 • Kanav Vats, Mehrnaz Fani, Pascale Walters, David A. Clausi, John Zelek
Experimental results demonstrate the effectiveness of the network by obtaining a 55% average F1 score on the NHL dataset and by achieving competitive performance compared to the state of the art on the SoccerNet dataset.
Ranked #4 on Action Spotting on SoccerNet
no code implementations • 11 Dec 2019 • Kanav Vats, William McNally, Chris Dulhanty, Zhong Qiu Lin, David A. Clausi, John Zelek
The network is able to regress the puck location from broadcast hockey video clips with varying camera angles.
no code implementations • 21 Apr 2019 • Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats, Jeffery Feng, Graham Taylor and, Alexander Wong
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability.
no code implementations • 24 Mar 2019 • Kanav Vats, Helmut Neher, Alexander Wong, David A. Clausi, John Zelek
This approach is motivated by the notion that rich contextual knowledge can be transferred between different keypoint subsets representing separate domains.
1 code implementation • 15 Mar 2019 • William McNally, Kanav Vats, Tyler Pinto, Chris Dulhanty, John McPhee, Alexander Wong
The golf swing is a complex movement requiring considerable full-body coordination to execute proficiently.
no code implementations • 22 Dec 2018 • Zixi Cai, Helmut Neher, Kanav Vats, David Clausi, John Zelek
Third, pose and optical flow streams are fused and passed to fully-connected layers to estimate the hockey player's action.