Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians

TL;DR

This paper introduces a personality-aware probabilistic map for pedestrian trajectory prediction, dynamically updating based on environment and pedestrian traits, leading to improved accuracy in diverse crowd scenarios.

Contribution

It proposes a novel personality-aware probabilistic map that enhances pedestrian trajectory prediction by incorporating personality traits and dynamic environment updates.

Findings

Improves prediction accuracy by 5-9% over prior algorithms.

Effective in both low and high-density crowd videos.

General approach applicable to standard human-trajectory datasets.

Abstract

We present a novel trajectory prediction algorithm for pedestrians based on a personality-aware probabilistic feature map. This map is computed using a spatial query structure and each value represents the probability of the predicted pedestrian passing through various positions in the crowd space. We update this map dynamically based on the agents in the environment and prior trajectory of a pedestrian. Furthermore, we estimate the personality characteristics of each pedestrian and use them to improve the prediction by estimating the shortest path in this map. Our approach is general and works well on crowd videos with low and high pedestrian density. We evaluate our model on standard human-trajectory datasets. In practice, our prediction algorithm improves the accuracy by 5-9% over prior algorithms.