An Evaluation of Trajectory Prediction Approaches and Notes on the TrajNet Benchmark

Abstract

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.

Accepted at ECCV Workshop on Anticipating Human Behavior under adapted title. RED: A simple but effective Baseline Predictor for the TrajNet Benchmark

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Cited by in corpus (14)

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• Spatial-Channel Transformer Network for Trajectory Prediction on the Traffic Scenes
• Predicting Responses to a Robot's Future Motion using Generative Recurrent Neural Networks
• COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos
• Pedestrian Trajectory Prediction using Context-Augmented Transformer Networks
• Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
• Exploring Dynamic Context for Multi-path Trajectory Prediction
• Spectral Temporal Graph Neural Network for Trajectory Prediction
• AMENet: Attentive Maps Encoder Network for Trajectory Prediction
• Evaluation metrics for behaviour modeling
• Predicting Human Trajectories by Learning and Matching Patterns