Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis

Braun, Marie-Sophie von; Frenzel, Patrick; Käding, Christian; Fuchs, Mirco

doi:10.1109/cvprw50498.2020.00446

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…Studies show that the CNN algorithm performs efficiently and exceptionally for spatial and temporal research problems. Moreover Von Braun et al (2020) employed Mask R‐CNN architecture to detect waterline detection in canoe sprint games. The authors employed a pretrained Mask R‐CNN architecture for canoe semantic segmentation and adopted a multistage approach to determine the waterline from the canoe segmentation.…”

Section: Methodologies In Sports Analyticsmentioning

confidence: 99%

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

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The rapid and impromptu interest in the coupling of machine learning (ML) algorithms with wearable and contactless sensors aimed at tackling real‐world problems warrants a pedagogical study to understand all the aspects of this research direction. Considering this aspect, this survey aims to review the state‐of‐the‐art literature on ML algorithms, methodologies, and hypotheses adopted to solve the research problems and challenges in the domain of sports. First, we categorize this study into three main research fields: sensors, computer vision, and wireless and mobile‐based applications. Then, for each of these fields, we thoroughly analyze the systems that are deployable for real‐time sports analytics. Next, we meticulously discuss the learning algorithms (e.g., statistical learning, deep learning, reinforcement learning) that power those deployable systems while also comparing and contrasting the benefits of those learning methodologies. Finally, we highlight the possible future open‐research opportunities and emerging technologies that could contribute to the domain of sports analytics.This article is categorized under: Technologies > Machine Learning Technologies > Artificial Intelligence Technologies > Internet of Things

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Section: Methodologies In Sports Analyticsmentioning

confidence: 99%

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Ghosh

Ramamurthy

Chakma

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…Shen et al [23] proposed an improved algorithm based on DeepLab v3 + for semantic segmentation of shoreline images to extract the water surface region, and when combined with the traditional edge detection algorithm to detect the water shoreline. Erdem et al [24] propose a majority voting method based on different deep learning structures to automatically acquire water shorelines Methods of deep learning are highly adaptable to a different times and shoreline scenes and can overcome the interference of uncontrollable factors such as reflection and ripple in shoreline scenes on image segmentation, and achieve accurate detection of water shoreline with fast extraction speed [25,26].…”

Section: Introductionmentioning

confidence: 99%

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Yin

Guo²,

Deng³

et al. 2022

Complex Intell. Syst.

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The water shoreline is essential for unmanned surface vessels (USVs) to navigate autonomously. Many existing traditional water shoreline detections approaches not only fail to overcome the effects of water reflections, image inversions, and other factors but are also unsuitable for water shoreline detection in a variety of weather conditions and in complex inland river scenarios. Therefore, we propose a water shoreline detection approach based on an enhanced Pyramid Scene Parsing Network (PSPNet). We introduce a migration learning approach to the PSPNet feature backbone extraction network Resnet50 to improve training efficiency and add a Convolutional Block Attention Module (CBAM) attention mechanism module to improve the robustness of training. In addition, the pyramid pooling module adds the branch of the atrous convolution module. Finally, the waterfront segmentation map is processed by the Canny edge detection method, which detects the water shorelines. For the network's training and validation, we use the USVInland dataset, the world's first urban inland driverless dataset. The experimental results show that the segmentation accuracy MIou of this paper is 96.87% and Accuracy is 98.41, which are higher than some mainstream algorithms. It is capable of detecting water shorelines accurately in a variety of interior river situations.

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“…In [25], in order to count the plants and calculate the size of plants, the image taken by a drone is processed by Mask R-CNN. In [26], using Mask R-CNN, the waterline is detected and analyzed in the sports area. In [27], it generates a synthetic dataset for scale-invariant instance segmentation of food materials using Mask R-CNN.…”

Section: Introductionmentioning

confidence: 99%

Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation

2022

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To prevent or deal with chronic diseases, using a smart device, automatically classifying food categories, estimating food volume and nutrients, and recording dietary intake are considered challenges. In this work, a novel real-time vision-based method for solid-volume food instance segmentation and calorie estimation is utilized, based on Mask R-CNN. In order to address the proposed method in real life, distinguishing it from other methods which use 3D LiDARs or RGB-D cameras, this work applies RGB images to train the model and uses a simple monocular camera to test the result. Gimbap is selected as an example of solid-volume food to show the utilization of the proposed method. Firstly, in order to improve detection accuracy, the proposed labeling approach for the Gimbap image datasets is introduced, based on the posture of Gimbap in plates. Secondly, an optimized model to detect Gimbap is created by fine-tuning Mask R-CNN architecture. After training, the model reaches AP (0.5 IoU) of 88.13% for Gimbap1 and AP (0.5 IoU) of 82.72% for Gimbap2. mAP (0.5 IoU) of 85.43% is achieved. Thirdly, a novel calorie estimation approach is proposed, combining the calibration result and the Gimbap instance segmentation result. In the fourth section, it is also shown how to extend the calorie estimation approach to be used in any solid-volume food, such as pizza, cake, burger, fried shrimp, oranges, and donuts. Compared with other food calorie estimation methods based on Faster R-CNN, the proposed method uses mask information and considers unseen food. Therefore, the method in this paper outperforms the accuracy of food segmentation and calorie estimation. The effectiveness of the proposed approaches is proven.

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Utilizing Mask R-CNN for Waterline Detection in Canoe Sprint Video Analysis

Cited by 9 publications

References 24 publications

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Sports analytics review: Artificial intelligence applications, emerging technologies, and algorithmic perspective

Improved PSPNet-based water shoreline detection in complex inland river scenarios

Utilizing Mask R-CNN for Solid-Volume Food Instance Segmentation and Calorie Estimation

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