Point-based Path Prediction from Polar Histograms
Pasquale Coscia, Francesco Castaldo, Francesco A.N. Palmieri, Lamberto Ballan, Alexandre Alahi and Silvio Savarese
19th International Conference on Information Fusion (FUSION 2016)

Abstract

We address the problem of modeling complex target behavior using a stochastic model that integrates object dynamics, statistics gathered from the environment and semantic knowledge about the scene. The method exploits prior knowledge to build point-wise polar histograms that provide the ability to forecast target motion to the most likely paths. Physical constraints are included in the model through a ray-launching procedure, while semantic scene segmentation is used to provide a coarser representation of the most likely crossable areas. The model is enhanced with statistics extracted from previously observed trajectories and with nearly-constant velocity dynamics. Information regarding the target’s destination may also be included steering the prediction to a predetermined area. Our experimental results, validated in comparison to actual targets’ trajectories, demonstrate that our approach can be effective in forecasting objects’ behavior in structured scenes.

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