|Jin Sun||Hadar Averbuch-Elor||Qianqian Wang||
Abstract. Predicting where people can walk in a scene is important for many tasks, including autonomous driving systems and human behavior analysis. Yet learning a computational model for this purpose is chal- lenging due to semantic ambiguity and a lack of labeled data: current datasets only tell you where people are, not where they could be. We tackle this problem by leveraging information from existing datasets, without additional labeling. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. However, this augmented data is still sparse. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss. Using this strategy, we demonstrate a model that learns to predict a walkability map from a single image. We evaluate our model on the Waymo and Cityscapes datasets, demonstrating superior performance compared to baselines and state-of-the-art models.
(a) Imagine an urban scene like this one. Where could people walk in such a scene? The answer is not simply ‘crosswalk’ or ‘sidewalk’—scene context plays a significant role. For example, people can be standing on sidewalks and not on crosswalks while waiting for a green light (b, top). A road can be open to walk, or only partially walkable (e.g., when occupied by cows, as in (b, bottom)). (c) We propose to predict where people could walk (highlighted in green) by learning from observing human behavior in weakly annotated data.
We leverage sequences collected by recent self-driving datasets such as the Waymo Open Dataset, without additional labeling. Our key observation is: a person’s 3D location in each frame is also a valid location in every other frame in the same sequence.
Given 3D labels, poses, and camera parameters, we propagate sparse ground-truth people locations to all frames: obtaining what we call Hidden Footprints.
We also propose a training strategy to learn from hidden footprints and predict walkability maps. The model generalizes well to diverse scenes and outperforms alternative approaches including semantics-based ones.
Below we show side-by-side video comparisons of input video sequences, propagated footprints, and our predicted heatmaps on the validation set of the Waymo dataset. Greener means higher score for walkability. Our model is able to predict reasonable walkable regions of diverse scenes, and under day/night conditions.
Please select different sequences below to play and click 'Play/Pause' for playback controls.
This research was supported in part by the generosity of Eric and Wendy Schmidt by recommendation of the Schmidt Futures program.