Phase Space Reconstruction Network for Lane Intrusion Action Recognition
| Autor: | Zhidong Deng, Hongchao Lu, Ruiwen Zhang, Hongsen Lin |
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| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Lyapunov function Computer science business.industry Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Process (computing) Normalization (image processing) Observable Object (computer science) symbols.namesake Phase space Classifier (linguistics) symbols Computer vision Artificial intelligence business Divergence (statistics) |
| Zdroj: | IJCNN |
| DOI: | 10.1109/ijcnn52387.2021.9534174 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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