Early Start Intention Detection of Cyclists Using Motion History Images and a Deep Residual Network

TL;DR

This paper introduces a deep residual neural network method using Motion History Images to detect cyclists' starting motions in real-time urban traffic, outperforming previous SVM-based approaches in accuracy and speed.

Contribution

The study presents a novel ResNet-based approach for cyclist start detection using MHIs, demonstrating superior performance over existing SVM methods.

Findings

ResNet approach achieved 100% F1-score.

ResNet detected starting motions on average in 0.144 seconds.

Outperformed SVM in robustness and detection speed.

Abstract

In this article, we present a novel approach to detect starting motions of cyclists in real world traffic scenarios based on Motion History Images (MHIs). The method uses a deep Convolutional Neural Network (CNN) with a residual network architecture (ResNet), which is commonly used in image classification and detection tasks. By combining MHIs with a ResNet classifier and performing a frame by frame classification of the MHIs, we are able to detect starting motions in image sequences. The detection is performed using a wide angle stereo camera system at an urban intersection. We compare our algorithm to an existing method to detect movement transitions of pedestrians that uses MHIs in combination with a Histograms of Oriented Gradients (HOG) like descriptor and a Support Vector Machine (SVM), which we adapted to cyclists. To train and evaluate the methods a dataset containing MHIs of 394 cyclist starting motions was created. The results show that both methods can be used to detect starting motions of cyclists. Using the SVM approach, we were able to safely detect starting motions 0.506 s on average after the bicycle starts moving with an F1-score of 97.7%. The ResNet approach achieved an F1-score of 100% at an average detection time of 0.144 s. The ResNet approach outperformed the SVM approach in both robustness against false positive detections and detection time.