A low complexity contextual stacked ensemble-learning approach for pedestrian intent prediction

TL;DR

This paper introduces a low-complexity ensemble-learning method that uses contextual data and skeleton-ization to accurately predict pedestrian crossing intent with significantly reduced computational requirements.

Contribution

It presents a novel, low-complexity stacked ensemble-learning approach incorporating contextual information and skeleton-ization for pedestrian intent prediction.

Findings

Achieves similar accuracy to state-of-the-art methods

Reduces computational complexity by 99.7%

Effective across multiple datasets

Abstract

Walking as a form of active travel is essential in promoting sustainable transport. It is thus crucial to accurately predict pedestrian crossing intention and avoid collisions, especially with the advent of autonomous and advanced driver-assisted vehicles. Current research leverages computer vision and machine learning advances to predict near-misses; however, this often requires high computation power to yield reliable results. In contrast, this work proposes a low-complexity ensemble-learning approach that employs contextual data for predicting the pedestrian's intent for crossing. The pedestrian is first detected, and their image is then compressed using skeleton-ization, and contextual information is added into a stacked ensemble-learning approach. Our experiments on different datasets achieve similar pedestrian intent prediction performance as the state-of-the-art approaches with 99.7% reduction in computational complexity. Our source code and trained models will be released upon paper acceptance