Abstract
Analysis of pedestrians’ motion is important to real-world applications in public scenes. Due to the complex temporal and spatial factors, trajectory prediction is a challenging task. With the development of attention mechanism recently, transformer network has been successfully applied in natural language processing, computer vision, and audio processing. We propose an end-to-end transformer network embedded with random deviation queries for pedestrian trajectory forecasting. The self-correcting scheme can enhance the robustness of the network. Moreover, we present a co-training strategy to improve the training effect. The whole scheme is trained collaboratively by the original loss and classification loss. Therefore, we also achieve more accurate prediction results. Experimental results on several datasets indicate the validity and robustness of the network. We achieve the best performance in individual forecasting and comparable results in social forecasting. Encouragingly, our approach achieves a new state of the art on the Hotel and Zara2 datasets compared with the social-based and individual-based approaches.
Highlights
Analysis of pedestrians’ motion is one of the core problems for many autonomous systems in public scenes, such as surveillance, crowd simulation, mobile robot navigation, and autonomous driving
Inspired by the great success of transformer in natural language processing [18,19], computer vision [20,22,23], and audio processing [21,24,25], we aim to implement trajectory forecasting with an end-to-end transformer model. Thanks to their better capability to learn non-linear patterns, we argue that transformer networks are suitable for sequence modeling and trajectories forecasting
We propose an effective and end-to-end trainable framework built upon the transformer framework which is embedded with a random deviation query for trajectory forecasting
Summary
Analysis of pedestrians’ motion is one of the core problems for many autonomous systems in public scenes, such as surveillance, crowd simulation, mobile robot navigation, and autonomous driving. It is essential for urban safety, and city planning. The pedestrian trajectory can be influenced by multiple factors, including individual moving style, the underlying destination, the motion of other agents, the environment topology structure, etc. The agents velocity, the interactions with other moving pedestrians affect the walking behavior of an individual. An efficient and effective end-to-end trainable framework is expected to improve pedestrian trajectory prediction performance
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