Learning nuisances to track pedestrians in autonomous vehicles

Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem as a data association one where they proposed various representations aiming for invariance to nuisances such as viewpoint changes, body deformation, object occlusion, and illumination changes. However, these methods still suffer to address abrupt changes since they do not explicitly model the nature of the nuisances. In this work, we propose to train a classifier that recognizes these nuisances, more specifically rotational body deformation of pedestrians. We aim to detect deformations as a method to find a good representation that will lead to better tracking of pedestrians as well as other tasks.