Human Trajectory Prediction using Spatially aware Deep Attention Models

TL;DR

This paper introduces a deep learning model that predicts human trajectories by incorporating spatial awareness and static scene features, outperforming previous methods on large datasets.

Contribution

It presents a novel end-to-end deep attention model that models static scene artifacts and handles multiple movement modes simultaneously.

Findings

Outperforms state-of-the-art on large-scale datasets

Effectively models static scene features for trajectory prediction

Extends to multiple movement modes

Abstract

Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence. Thanks to a large number of applications like predicting abnormal events, navigation system for the blind, etc. there have been many approaches to attempt learning patterns of motion directly from data using a wide variety of techniques ranging from hand-crafted features to sophisticated deep learning models for unsupervised feature learning. All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects. We propose an end to end deep learning model to learn the motion patterns of humans using different navigational modes directly from data using the much popular sequence to sequence model coupled with a soft attention mechanism. We also propose a novel approach to model the static artefacts in a scene and using these to predict the dynamic trajectories. The proposed method, tested on trajectories of pedestrians, consistently outperforms previously proposed state of the art approaches on a variety of large scale data sets. We also show how our architecture can be naturally extended to handle multiple modes of movement (say pedestrians, skaters, bikers and buses) simultaneously.