Behavioral-Aware Early Destination Prediction Using LiDAR Point Clouds and Movement Trajectories

Large-scale facility management increasingly relies on understanding human movement patterns, yet existing person re-identification (Re-ID) methods face challenges in tracking individuals across non-overlapping sensor regions. This paper presents a novel approach combining 3D LiDAR point cloud data with trajectory information to predict pedestrian destinations in early stages of movement. By predicting likely destinations, our method can significantly narrow down the search space for matching individuals between different sensor regions, thereby improving Re-ID accuracy. Our method leverages deep learning architectures—PointNet for processing point cloud data and LSTM for analyzing trajectory patterns—to capture both instantaneous behavioral characteristics and temporal movement patterns. Through experiments conducted with 33,110 trajectories in a university campus setting, our combined approach achieved 90.0% accuracy in predicting three destination categories (entrance, elevator/stairs, and meeting rooms), significantly out-performing single-modality approaches. The results demonstrate that early behavioral cues indeed carry significant predictive power for destination estimation. These findings have important implications for improving person tracking across distributed sensor networks and enabling proactive facility management systems.