Pedestrian Trajectory Prediction Method Based on Feature Fusion

Pedestrian trajectory prediction has broad application prospects in multiple fields. It can be integrated into various instruments and meters, such as millimeter-wave radars and LiDAR devices, as well as camera surveillance equipment in intelligent security systems. Currently, many trajectory prediction methods use overhead camera sensors to obtain scene information and historical trajectory data. But they also have some shortcomings. On one hand, there is the challenge of how to effectively integrate feature information and capture the complex interactions between data. On the other hand, there is the issue of how to accurately measure the difference between the model’s predicted values and the actual values and reduce cumulative errors during training. The commonly used concatenation fusion method in some approaches may fail to fully consider the intrinsic correlations and differences among different features, simply concatenating them together. Therefore, in this article, we adopt the average fusion method to integrate static scene information from overhead cameras, historical trajectory information of pedestrians, and social interaction information. This method can reduce redundant information and allow implicit interactions among feature information within the model, thereby capturing more complex data relationships. Second, we embed the information fractal equation into the loss function of the model. This fractal loss term is designed to measure the detail loss in the model, reduce the accumulated errors during training, and enhance prediction accuracy. Finally, we use generative adversarial network (GAN) to generate more realistic trajectories. Experiments on the ETH/UCY dataset demonstrate a significant improvement in trajectory prediction accuracy, particularly in complex situations. Compared with the baseline model, the proposed method reduces errors by 14.8% in average displacement error (ADE) and by 25.2% in final displacement error (FDE). Furthermore, the model’s training time is also effectively reduced.