Affiliation:
1. University of North Carolina at Chapel Hill
Abstract
We present a real-time algorithm to automatically classify the behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian and computes its motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics and use them for crowd prediction. Our learning scheme is general and we highlight its performance in identifying the personality of different pedestrians in low and high density crowd videos. We also evaluate the accuracy by comparing the results with a user study.
Publisher
International Joint Conferences on Artificial Intelligence Organization
Cited by
12 articles.
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1. Channel spatio-temporal convolutional network for pedestrian trajectory prediction;International Journal of Machine Learning and Cybernetics;2024-06-20
2. Channel Spatio-Temporal Convolutional Network for Trajectory Prediction;Communications in Computer and Information Science;2024
3. Autonomous Robot Navigation in Crowd;2022 Latin American Robotics Symposium (LARS), 2022 Brazilian Symposium on Robotics (SBR), and 2022 Workshop on Robotics in Education (WRE);2022-10-18
4. Mixed Reality Agent-Based Framework for Pedestrian-Cyclist Interaction;2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct);2022-10
5. State Estimation and Motion Prediction of Vehicles and Vulnerable Road Users for Cooperative Autonomous Driving: A Survey;IEEE Transactions on Intelligent Transportation Systems;2022-10