基于深度神经网络的山地自行车下坡道难度分类
Difficulty Classification of Mountainbike Downhill Trails utilizing Deep Neural Networks
摘要：
山地自行车下坡道的难度是一种主观感受。然而，体育协会和山地自行车公园运营商试图将路径分组为不同难度级别，如Singletrail-Skala（S 0-S5）或彩色尺度（蓝色，红色，黑色，...）。国际山地自行车协会（International Mountain Bicycling Association）。难度分级的增加是由于各种尺度、不同的人对小径进行分级、地形的差异等等。我们提出了一种端到端的深度学习方法，通过使用传感器数据将踪迹分为三个难度：容易，中等和困难。使用mbientlab Meta Motion r0.2传感器单元，我们可以记录一名骑手在多个赛道段上的加速度计和陀螺仪数据。用上述数据的堆叠和级联表示作为其输入来训练2D卷积神经网络。我们使用五种不同的样本和五种不同的内核大小进行实验，并实现了0.9097的最大稀疏分类精度。据我们所知，这是第一个针对计算难度分类的山地自行车下坡道的工作。
英文摘要：
The difficulty of mountainbike downhill trails is a subjective perception. However, sports-associations and mountainbike park operators attempt to group trails into different levels of difficulty with scales like the Singletrail-Skala (S0-S5) or colored scales (blue, red, black, ...) as proposed by The International Mountain Bicycling Association. Inconsistencies in difficulty grading occur due to the various scales, different people grading the trails, differences in topography, and more. We propose an end-to-end deep learning approach to classify trails into three difficulties easy, medium, and hard by using sensor data. With mbientlab Meta Motion r0.2 sensor units, we record accelerometer- and gyroscope data of one rider on multiple trail segments. A 2D convolutional neural network is trained with a stacked and concatenated representation of the aforementioned data as its input. We run experiments with five different sample- and five different kernel sizes and achieve a maximum Sparse Categorical Accuracy of 0.9097. To the best of our knowledge, this is the first work targeting computational difficulty classification of mountainbike downhill trails.
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