Abstract. Humans navigate crowded spaces such as a university campus by following common sense rules based on social etiquette. In this paper, we argue that in order to enable the design of new target tracking or trajectory forecasting methods that can take full advantage of these rules, we need to have access to better data in the first place. To that end, we contribute a new large-scale dataset that collects videos of various types of targets (not just pedestrians, but also bikers, skateboarders, cars, buses, golf carts) that navigate in a real world outdoor environment such as a university campus. Moreover, we introduce a new characterization that describes the "social sensitivity" at which two targets interact. We use this characterization to define "navigation styles" and improve both forecasting models and state-of-the-art multi-target tracking -whereby the learnt forecasting models help the data association step.
Robicquet et al. REF introduce a UAV dataset (Stanford) that collects images and videos of six types of objects in the Stanford campus area.
Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
ECCV
Computer Science