Learning to Forecast Pedestrian Intention from Pose Dynamics

For an autonomous car, the ability to foresee a humans action is very useful for mitigating the risk of a possible collision. To humans this pedestrian intention foresight comes naturally as they are able to recognize another person's actions just by perceiving subtle changes in posture. Approximating this intention inference ability by directly training a deep neural network is useful but especially challenging. First, sufficiently large datasets for intention recognition with frame-wise human pose and intention annotations are rare and expensive to compile. Second, training on smaller datasets can lead to overfitting and make it difficult to adapt to intra-class variations in action executions. Therefore, in this paper, we propose a real time framework that learns (i) intention recognition using weak-supervision and (ii) locomotion dynamics of intention from pose information using transfer learning. This new formulation is able to tackle the lack of frame-wise annotations and to learn intra-class variation in action executions. We empirically demonstrate that our proposed approach leads to earlier and more stable detection of intention than other state of the art approaches with real time operation and the ability to detect intention one second before the pedestrian reaches the kerb.