Learning Patterns of Motion Trajectories Using Real-Time Tracking

Ying Feng Cai; Hai Wang; Wei Gong Zhang

doi:10.4028/www.scientific.net/AMR.403-408.2768

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The Wireless Application of Space-Time Spread-Spectrum Technology in the Internet of Things
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A Survey: Clustering Ensemble Selection
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Spatial Filtering Collimator for Optimized Laser Indicator and Laser Range Finder
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Learning Patterns of Motion Trajectories Using Real-Time Tracking
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Study on Wireless Sensor Network Based on Laser Rangefinder
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 403-408Learning Patterns of Motion Trajectories Using...

Learning Patterns of Motion Trajectories Using Real-Time Tracking

Abstract:

The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Experiments in actual road scene show the effectiveness of the proposed algorithm.

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Periodical:

Advanced Materials Research (Volumes 403-408)

Pages:

2768-2771

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.403-408.2768

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Online since:

November 2011

Authors:

Ying Feng Cai, Hai Wang, Wei Gong Zhang

Keywords:

Activity Patterns, Anomaly Prediction, Intelligent Visual Surveillance System, Self-Organizing Neural Network

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