论文标题
木匠海报：实时行人路径预测的卷积方法
CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
论文作者
论文摘要
行人路径预测是计算机视觉和视频理解中的重要主题。深入了解行人的行动对于确保在包括自动驾驶汽车，社交机器人和环境监控在内的各种应用中安全操作至关重要。当前在该领域的工作利用复杂的生成或经常性方法来捕获许多可能的未来。但是，尽管预测未来路径的固有实时性质，但几乎没有做过探索该任务准确且计算高效的方法的工作。为此，我们提出了一种实时行人路径预测的卷积方法。它利用图形同构网络的变化与敏捷卷积神经网络设计结合使用，形成快速准确的路径预测方法。在推理速度和预测准确性方面都值得注意的结果，与当前的最新方法相比，在众所周知的路径预测数据集上提供了竞争精度，从而大大提高了FPS。
Pedestrian path prediction is an essential topic in computer vision and video understanding. Having insight into the movement of pedestrians is crucial for ensuring safe operation in a variety of applications including autonomous vehicles, social robots, and environmental monitoring. Current works in this area utilize complex generative or recurrent methods to capture many possible futures. However, despite the inherent real-time nature of predicting future paths, little work has been done to explore accurate and computationally efficient approaches for this task. To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe. It utilizes a variation of Graph Isomorphism Networks in combination with an agile convolutional neural network design to form a fast and accurate path prediction approach. Notable results in both inference speed and prediction accuracy are achieved, improving FPS considerably in comparison to current state-of-the-art methods while delivering competitive accuracy on well-known path prediction datasets.
