Phase Space Reconstruction Network for Lane Intrusion Action Recognition

• URL: http://arxiv.org/abs/2102.11149v1
• Date: Mon, 22 Feb 2021 16:18:35 GMT
• Title: Phase Space Reconstruction Network for Lane Intrusion Action Recognition
• Authors: Ruiwen Zhang and Zhidong Deng and Hongsen Lin and Hongchao Lu
• Abstract summary: In this paper, we propose a novel object-level phase space reconstruction network (PSRNet) for motion time series classification. Our PSRNet could reach the best accuracy of 98.0%, which remarkably exceeds existing action recognition approaches by more than 30%.
• Score: 9.351931162958465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a complex road traffic scene, illegal lane intrusion of pedestrians or cyclists constitutes one of the main safety challenges in autonomous driving application. In this paper, we propose a novel object-level phase space reconstruction network (PSRNet) for motion time series classification, aiming to recognize lane intrusion actions that occur 150m ahead through a monocular camera fixed on moving vehicle. In the PSRNet, the movement of pedestrians and cyclists, specifically viewed as an observable object-level dynamic process, can be reconstructed as trajectories of state vectors in a latent phase space and further characterized by a learnable Lyapunov exponent-like classifier that indicates discrimination in terms of average exponential divergence of state trajectories. Additionally, in order to first transform video inputs into one-dimensional motion time series of each object, a lane width normalization based on visual object tracking-by-detection is presented. Extensive experiments are conducted on the THU-IntrudBehavior dataset collected from real urban roads. The results show that our PSRNet could reach the best accuracy of 98.0%, which remarkably exceeds existing action recognition approaches by more than 30%.

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