MonoPIC -- A Monocular Low-Latency Pedestrian Intention Classification Framework for IoT Edges Using ID3 Modelled Decision Trees

TL;DR

This paper introduces MonoPIC, a low-latency pedestrian intention classification framework for IoT edges that uses decision trees and pose estimation to improve reaction times in autonomous vehicles.

Contribution

It presents a novel, fast, and resource-efficient algorithm for pedestrian intent classification that bypasses deep learning, suitable for IoT edge devices.

Findings

Achieved 83.56% accuracy in pedestrian intent classification.

Operates with an average latency of 48 milliseconds.

Outperforms traditional spatio-temporal convolutional networks in speed and resource usage.

Abstract

Road accidents involving autonomous vehicles commonly occur in situations where a (pedestrian) obstacle presents itself in the path of the moving vehicle at very sudden time intervals, leaving the robot even lesser time to react to the change in scene. In order to tackle this issue, we propose a novel algorithmic implementation that classifies the intent of a single arbitrarily chosen pedestrian in a two dimensional frame into logic states in a procedural manner using quaternions generated from a MediaPipe pose estimation model. This bypasses the need to employ any relatively high latency deep-learning algorithms primarily due to the lack of necessity for depth perception as well as an implicit cap on the computational resources that most IoT edge devices present. The model was able to achieve an average testing accuracy of 83.56% with a reliable variance of 0.0042 while operating with an average latency of 48 milliseconds, demonstrating multiple notable advantages over the current standard of using spatio-temporal convolutional networks for these perceptive tasks.