Personality-Aware Probabilistic Map for Trajectory Prediction of Pedestrians
This work addresses trajectory prediction for pedestrians in crowds, which is an incremental advancement in autonomous navigation and surveillance systems.
The paper tackles pedestrian trajectory prediction by introducing a personality-aware probabilistic map that dynamically updates based on environmental agents and prior trajectories, resulting in a 5-9% accuracy improvement over prior algorithms.
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.