Bicycle rider pose estimation using a human skeleton model with bicycle parts

We propose a method for estimating the pose of a bicycle rider from images by integrating the relationship between bicycle parts and the human body and modeling them as a single skeleton for traffic analysis. The feature of this method is that the bicycle rider and the bicycle are represented as an integrated skeletal model instead of separating the skeletal representation of the bicycle parts and the human body. We call this the Bicycle Rider Model. To train the Bicycle Rider Model, we handcrafted our video dataset. In the dataset, we annotate the center of the handlebars, the crankshaft, and the front end of the front wheel as the new keypoints of the skeleton. We confirmed that the Bicycle Rider Model can estimate the pose of the bicycle rider.