Trajectory Prediction using Attentive Visual Features
Sungchan Oh, Jinyoung Moon · 2024
This research paper presents a new method for predicting future trajectories of objects in video. Previous studies have mainly focused on the spatial attributes of objects, such as their bounding boxes or coordinates, while often overlooking visual features of the objects and surroundings. Our approach overcomes this limitation by incorporating visual feature extraction networks with trajectory prediction networks, resulting in a significant improvement in predictive accuracy. We conducted extensive testing on trajectory datasets captured from both first-person and bird’s eye views, which validated our method and demonstrated a notable improvement in prediction accuracy. These results confirm the effectiveness of our integrated visual feature extraction in improving trajectory prediction models and emphasize the importance of considering dynamic relationships between objects and their surroundings for more precise predictions.