Pedestrian Motion State Estimation From 2D Pose

TL;DR

This paper introduces a computationally efficient method using a gated recurrent neural network to estimate pedestrian motion states from 2D pose data, improving accuracy for safer road behavior prediction.

Contribution

It presents a novel approach combining micro motion features and a seq2seq model for pedestrian motion state estimation, enhancing accuracy over existing methods.

Findings

Accuracy improved by 11.6% on JAAD dataset

Uses micro motion features like position, angle, and distance

Employs a GRU-based seq2seq model for state transition learning

Abstract

Traffic violation and the flexible and changeable nature of pedestrians make it more difficult to predict pedestrian behavior or intention, which might be a potential safety hazard on the road. Pedestrian motion state (such as walking and standing) directly affects or reflects its intention. In combination with pedestrian motion state and other influencing factors, pedestrian intention can be predicted to avoid unnecessary accidents. In this paper, pedestrian is treated as non-rigid object, which can be represented by a set of two-dimensional key points, and the movement of key point relative to the torso is introduced as micro motion. Static and dynamic micro motion features, such as position, angle and distance, and their differential calculations in time domain, are used to describe its motion pattern. Gated recurrent neural network based seq2seq model is used to learn the dependence of motion state transition on previous information, finally the pedestrian motion state is estimated via a softmax classifier. The proposed method only needs the previous hidden state of GRU and current feature to evaluate the probability of current motion state, and it is computation efficient to deploy on vehicles. This paper verifies the proposed algorithm on the JAAD public dataset, and the accuracy is improved by 11.6% compared with the existing method.