  In this article, we present a novel approach to detect starting motions of
cyclists in real world traffic scenarios based on Motion History Images (MHIs).
The method uses a deep Convolutional Neural Network (CNN) with a residual
network architecture (ResNet), which is commonly used in image classification
and detection tasks. By combining MHIs with a ResNet classifier and performing
a frame by frame classification of the MHIs, we are able to detect starting
motions in image sequences. The detection is performed using a wide angle
stereo camera system at an urban intersection. We compare our algorithm to an
existing method to detect movement transitions of pedestrians that uses MHIs in
combination with a Histograms of Oriented Gradients (HOG) like descriptor and a
Support Vector Machine (SVM), which we adapted to cyclists. To train and
evaluate the methods a dataset containing MHIs of 394 cyclist starting motions
was created. The results show that both methods can be used to detect starting
motions of cyclists. Using the SVM approach, we were able to safely detect
starting motions 0.506 s on average after the bicycle starts moving with an
F1-score of 97.7%. The ResNet approach achieved an F1-score of 100% at an
average detection time of 0.144 s. The ResNet approach outperformed the SVM
approach in both robustness against false positive detections and detection
time.
