  In this work, orientation detection using Deep Learning is acknowledged for a
particularly vulnerable class of road users,the cyclists. Knowing the cyclists'
orientation is of great relevance since it provides a good notion about their
future trajectory, which is crucial to avoid accidents in the context of
intelligent transportation systems. Using Transfer Learning with pre-trained
models and TensorFlow, we present a performance comparison between the main
algorithms reported in the literature for object detection,such as SSD, Faster
R-CNN and R-FCN along with MobilenetV2, InceptionV2, ResNet50, ResNet101
feature extractors. Moreover, we propose multi-class detection with eight
different classes according to orientations. To do so, we introduce a new
dataset called "Detect-Bike", containing 20,229 cyclist instances over 11,103
images, which has been labeled based on cyclist's orientation. Then, the same
Deep Learning methods used for detection are trained to determine the target's
heading. Our experimental results and vast evaluation showed satisfactory
performance of all of the studied methods for the cyclists and their
orientation detection, especially using Faster R-CNN with ResNet50 proved to be
precise but significantly slower. Meanwhile, SSD using InceptionV2 provided
good trade-off between precision and execution time, and is to be preferred for
real-time embedded applications.
