Human trajectory prediction using LSTM with Attention mechanism

TL;DR

This paper introduces an LSTM-based human trajectory prediction model enhanced with an attention mechanism that prioritizes relevant input features, leading to improved accuracy over existing models like Social LSTM.

Contribution

The paper presents a novel neural layer that integrates attention scores into trajectory prediction, improving prediction accuracy on ETH and UCY datasets.

Findings

Achieves 6.2% improvement in ADE over Social LSTM

Achieves 6.3% improvement in FDE over Social LSTM

Outperforms existing models in crowded space scenarios

Abstract

In this paper, we propose a human trajectory prediction model that combines a Long Short-Term Memory (LSTM) network with an attention mechanism. To do that, we use attention scores to determine which parts of the input data the model should focus on when making predictions. Attention scores are calculated for each input feature, with a higher score indicating the greater significance of that feature in predicting the output. Initially, these scores are determined for the target human position, velocity, and their neighboring individual's positions and velocities. By using attention scores, our model can prioritize the most relevant information in the input data and make more accurate predictions. We extract attention scores from our attention mechanism and integrate them into the trajectory prediction module to predict human future trajectories. To achieve this, we introduce a new neural layer that processes attention scores after extracting them and concatenates them with positional information. We evaluate our approach on the publicly available ETH and UCY datasets and measure its performance using the final displacement error (FDE) and average displacement error (ADE) metrics. We show that our modified algorithm performs better than the Social LSTM in predicting the future trajectory of pedestrians in crowded spaces. Specifically, our model achieves an improvement of 6.2% in ADE and 6.3% in FDE compared to the Social LSTM results in the literature.