Video and Sensor-Based Rope Pulling Detection in Sport Climbing

Sport climbing is becoming an increasingly popular competitive sport as well as a recreational activity. For this reason, indoor sport climbing operators are constantly trying to improve their services and optimally use their infrastructure. One way to support such a task is to track the climbing activities performed by visitors while climbing. This paper considers a scenario in which a sensor is attached to a piece of climbing equipment that connects the climbing rope to the bolt anchors (quickdraws) and a camera is overlooking a climbing wall. Within this scenario, this paper explores two approaches to detect when a climber finishes a climb and pulls the rope from the wall: 1) a hybrid approach in which sensors and cameras are used and 2) a video-based approach where only cameras are used. The evaluation resulted in recognition precision of 91% for the hybrid and 76% for the video-based approach, respectively. This paper also discusses advantages and disadvantages of analysed approaches and points out future research directions to allow the automatic tracking of climbing activities.

[1]  IlJu Ko,et al.  A climbing motion recognition method using anatomical information for screen climbing games , 2017, Human-centric Computing and Information Sciences.

[2]  Bernhard Sick,et al.  Performing event detection in time series with SwiftEvent: an algorithm with supervised learning of detection criteria , 2018, Pattern Analysis and Applications.

[3]  Chittaranjan A. Mandal,et al.  Automatic Detection of Human Fall in Video , 2007, PReMI.

[4]  Ross B. Girshick,et al.  Focal Loss for Dense Object Detection , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Сергей Евгеньевич Штехин,et al.  Разработка алгоритма распознавания движений человека методами компьютерного зрения в задаче нормирования рабочего времени , 2020 .

[6]  Hiroshi Mizoguchi,et al.  Detecting and modeling play behavior using sensor-embedded rock-climbing equipment , 2010, IDC.

[7]  Jan Berg,et al.  Digiwall: an interactive climbing wall , 2005, ACE '05.

[8]  Dongho Kim,et al.  Analysis of Climbing Postures and Movements in Sport Climbing for Realistic 3D Climbing Animations , 2015 .

[9]  Dan Zecha,et al.  Frame-Level Event Detection in Athletics Videos with Pose-Based Convolutional Sequence Networks , 2019, MMSports '19.

[10]  Gustavo Carneiro,et al.  Probabilistic Object Detection: Definition and Evaluation , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[11]  Franz Konstantin Fuss,et al.  Instrumented climbing holds and performance analysis in sport climbing , 2008 .

[12]  Kevin C. Phillips,et al.  Optimizing Rock Climbing Performance Through Sport-Specific Strength and Conditioning , 2012 .

[13]  Diane J. Cook,et al.  Simple and Complex Activity Recognition through Smart Phones , 2012, 2012 Eighth International Conference on Intelligent Environments.

[14]  Theodore Lim,et al.  FreezeOut: Accelerate Training by Progressively Freezing Layers , 2017, NIPS 2017.

[15]  Hadi Seyedarabi,et al.  Pose estimation of soccer players using multiple uncalibrated cameras , 2015, Multimedia Tools and Applications.

[16]  Paul J. M. Havinga,et al.  Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey , 2010, ARCS Workshops.

[17]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[18]  Andrea Bergmann.  Engineering Of Sport 6 , 2016 .

[19]  Héctor Pomares,et al.  Window Size Impact in Human Activity Recognition , 2014, Sensors.

[20]  Matti Pietikäinen,et al.  Deep Learning for Generic Object Detection: A Survey , 2018, International Journal of Computer Vision.

[21]  Duc A. Tran,et al.  The 11th International Conference on Mobile Systems and Pervasive Computing (MobiSPC-2014) A Study on Human Activity Recognition Using Accelerometer Data from Smartphones , 2014 .

[22]  Soraia Raupp Musse,et al.  Crowd Analysis Using Computer Vision Techniques , 2010, IEEE Signal Processing Magazine.

[23]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[24]  Florian Daiber,et al.  ClimbSense: Automatic Climbing Route Recognition using Wrist-worn Inertia Measurement Units , 2015, CHI.

[25]  I Frosio,et al.  3D analysis of the body center of mass in rock climbing. , 2007, Human movement science.

[26]  Eamonn J. Keogh,et al.  Segmenting Time Series: A Survey and Novel Approach , 2002 .

[27]  Romain Hérault,et al.  Automatic Sensor-Based Detection and Classification of Climbing Activities , 2015, IEEE Sensors Journal.

[28]  Guang-Zhong Yang,et al.  ClimBSN: Climber performance monitoring with BSN , 2008, 2008 5th International Summer School and Symposium on Medical Devices and Biosensors.

[29]  Alexandr A. Kalinin,et al.  Albumentations: fast and flexible image augmentations , 2018, Inf..

[30]  Theodore Berger,et al.  Intelligent fence intrusion detection system: detection of intentional fence breaching and recognition of fence climbing , 2008, 2008 IEEE Conference on Technologies for Homeland Security.

[31]  Alberto Del Bimbo,et al.  Semantic annotation of soccer videos: automatic highlights identification , 2003, Comput. Vis. Image Underst..

[32]  Perttu Hämäläinen,et al.  Augmented climbing: interacting with projected graphics on a climbing wall , 2014, CHI Extended Abstracts.