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.

本文提出了一种基于卷积神经网络和图同构网络的实时行人路径预测方法，名为 CARPe，具有提高预测精度和实时性的优点。

CARPe Posterum：一种用于实时行人路径预测的卷积方法

CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path  Prediction

According to several reports published by worldwide organisations, thousands
of pedestrians die in road accidents every year. Due to this fact, vehicular
technologies have been evolving with the intent of reducing these fatalities.
This evolution has not finished yet since, for instance, the predictions of
pedestrian paths could improve the current Automatic Emergency Braking Systems
(AEBS). For this reason, this paper proposes a method to predict future
pedestrian paths, poses and intentions up to 1s in advance. This method is
based on Balanced Gaussian Process Dynamical Models (B-GPDMs), which reduce the
3D time-related information extracted from keypoints or joints placed along
pedestrian bodies into low-dimensional spaces. The B-GPDM is also capable of
inferring future latent positions and reconstruct their associated
observations. However, learning a generic model for all kind of pedestrian
activities normally provides less ccurate predictions. For this reason, the
proposed method obtains multiple models of four types of activity, i.e.
walking, stopping, starting and standing, and selects the most similar model to
estimate future pedestrian states. This method detects starting activities
125ms after the gait initiation with an accuracy of 80% and recognises stopping
intentions 58.33ms before the event with an accuracy of 70%. Concerning the
path prediction, the mean error for stopping activities at a Time-To-Event
(TTE) of 1s is 238.01mm and, for starting actions, the mean error at a TTE of
0s is 331.93mm.

研究提出了一种基于均衡高斯过程动态模型的方法，用于预测未来的行人路径和意图，并支持实时的活动类型预测以改进自动紧急制动系统，并取得了较高的准确率。

高斯过程动力学模型与行人活动识别用于行人路径、姿态和意图预测

Pedestrian Path, Pose and Intention Prediction through Gaussian Process  Dynamical Models and Pedestrian Activity Recognition

In recent years, there is a shift from modeling the tracking problem based on
Bayesian formulation towards using deep neural networks. Towards this end, in
this paper the effectiveness of various deep neural networks for predicting
future pedestrian paths are evaluated. The analyzed deep networks solely rely,
like in the traditional approaches, on observed tracklets without human-human
interaction information. The evaluation is done on the publicly available
TrajNet benchmark dataset, which builds up a repository of considerable and
popular datasets for trajectory-based activity forecasting. We show that a
Recurrent-Encoder with a Dense layer stacked on top, referred to as
RED-predictor, is able to achieve sophisticated results compared to elaborated
models in such scenarios. Further, we investigate failure cases and give
explanations for observed phenomena and give some recommendations for
overcoming demonstrated shortcomings.

本文旨在评估各种深度神经网络在预测未来行人路径方面的有效性，使用公共数据集 TrajNet 进行评估，其中使用的分析深度网络仅依赖于观察到的轨迹，研究结果表明，RED-predictor 的表现比其他精细模型更加出色。同时探究了失败案例的原因并提出了建议。