Search Results for author:
Found 23 papers, 2 papers with code
Multi Player Tracking in Ice Hockey with Homographic Projections
Multi Object Tracking (MOT) in ice hockey pursues the combined task of localizing and associating players across a given sequence to maintain their identities.
Region-level labels in ice charts can produce pixel-level segmentation for Sea Ice types
Fully supervised deep learning approaches have demonstrated impressive accuracy in sea ice classification, but their dependence on high-resolution labels presents a significant challenge due to the difficulty of obtaining such data.
Evaluating deep tracking models for player tracking in broadcast ice hockey video
Tracking and identifying players is an important problem in computer vision based ice hockey analytics.
Understanding the Effects of Second-Order Approximations in Natural Policy Gradient Reinforcement Learning
Natural policy gradient methods are popular reinforcement learning methods that improve the stability of policy gradient methods by utilizing second-order approximations to precondition the gradient with the inverse of the Fisher-information matrix.
Ice hockey player identification via transformers and weakly supervised learning
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
M2A: Motion Aware Attention for Accurate Video Action Recognition
We further compared the performance of M2A with other state-of-the-art motion and attention mechanisms on the Something-Something V1 video action recognition benchmark.
Player Tracking and Identification in Ice Hockey
The player identification model further takes advantage of the available NHL game roster data to obtain a player identification accuracy of 83%.
Multi-task learning for jersey number recognition in Ice Hockey
Identifying players in sports videos by recognizing their jersey numbers is a challenging task in computer vision.
Sentinel-1 Additive Noise Removal from Cross-Polarization Extra-Wide TOPSAR with Dynamic Least-Squares
We consider a linear denoising model that re-scales the noise field for each subswath, whose parameters are found from a least-squares solution over the objective function.
Puck localization and multi-task event recognition in broadcast hockey videos
In this paper, we introduce and implement a network for puck localization in broadcast hockey video.
Localization of Ice-Rink for Broadcast Hockey Videos
To localize the frames on the ice-rink model, a ResNet18-based regressor is implemented and trained, which regresses to four control points on the model in a frame-by-frame fashion.
Event detection in coarsely annotated sports videos via parallel multi receptive field 1D convolutions
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
PuckNet: Estimating hockey puck location from broadcast video
The network is able to regress the puck location from broadcast hockey video clips with varying camera angles.
Generative Adversarial Networks and Conditional Random Fields for Hyperspectral Image Classification
In this paper, we address the hyperspectral image (HSI) classification task with a generative adversarial network and conditional random field (GAN-CRF) -based framework, which integrates a semi-supervised deep learning and a probabilistic graphical model, and make three contributions.
KPTransfer: improved performance and faster convergence from keypoint subset-wise domain transfer in human pose estimation
This approach is motivated by the notion that rich contextual knowledge can be transferred between different keypoint subsets representing separate domains.
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Conclusion: The SISC radiomic sequencer is able to achieve state-of-the-art results in lung cancer prediction, and also offers prediction interpretability in the form of critical response maps.
Spatial probabilistic pulsatility model for enhancing photoplethysmographic imaging systems
Here, we developed a continuous probabilistic pulsatility model for importance-weighted blood pulse waveform extraction.
A spectral-spatial fusion model for robust blood pulse waveform extraction in photoplethysmographic imaging
Here, we design and implement a signal fusion framework, FusionPPG, for extracting a blood pulse waveform signal with strong temporal fidelity from a scene without requiring anatomical priors (e. g., facial tracking).
Non-contact hemodynamic imaging reveals the jugular venous pulse waveform
Here, we demonstrate for the first time that the JVP can be consistently observed in a non-contact manner using a novel light-based photoplethysmographic imaging system, coded hemodynamic imaging (CHI).
Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling
A numerical demultiplexer is then learned via non-linear random forest modeling based on the forward model.
Non-contact transmittance photoplethysmographic imaging (PPGI) for long-distance cardiovascular monitoring
The results support the hypothesis that long-distance heart rate monitoring is feasible using transmittance PPGI, allowing for new possibilities of monitoring cardiovascular function in a non-contact manner.
Statistical Textural Distinctiveness for Salient Region Detection in Natural Images
A novel statistical textural distinctiveness approach for robustly detecting salient regions in natural images is proposed.
Unsupervised image segmentation using a simple MRF model with a new implementation scheme
Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results.
