  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%.
