The head pose is an important cue for computer vision. Traditionally considered in human computer interaction applications,
it becomes very hard to model in surveillance scenarios, due to the tiny head size. Additionally, no public dataset contains continuous head pose annotations in open scenery, making the challenge even harder to face. Here we present a
framework based on Faster RCNN, which introduces a branch in the network architecture related to the head pose estimation.
The key idea is to leverage the presence of the people body to better infer the head pose, through a joint optimization
process. Additionally, we enrich the Town Center dataset with head pose labels, promoting further study on this topic.
Results on this novel benchmark and ablation studies on other task-specific datasets promote our idea and confirm the importance of the body cues to contextualize the head pose estimation.