Traffic Pattern Analysis and Anomaly Detection via Probabilistic Inference Model

2015 ◽  
pp. 215-240
Author(s):  
Hawook Jeong ◽  
Youngjoon Yoo ◽  
Kwang Moo Yi ◽  
Jin Young Choi
Keyword(s):  
Anomaly Detection ◽  
Pattern Analysis ◽  
Probabilistic Inference ◽  
Inference Model ◽  
Traffic Pattern

Two-stage online inference model for traffic pattern analysis and anomaly detection

2014 ◽  
Vol 25 (6) ◽  
pp. 1501-1517 ◽  
Author(s):  
Hawook Jeong ◽  
Youngjoon Yoo ◽  
Kwang Moo Yi ◽  
Jin Young Choi
Keyword(s):  
Anomaly Detection ◽  
Pattern Analysis ◽  
Inference Model ◽  
Two Stage ◽  
Traffic Pattern ◽  
Online Inference

scholarly journals Traffic Pattern Analysis in a Flight Simulator: Subjective and Physiological Mental Workload Assessment Techniques

2018 ◽  
Vol 12 ◽  
Author(s):  
Raphaëlle Roy ◽  
Benjamin Winkler ◽  
Fabian Honecker ◽  
Sébastien Scannella ◽  
Frédéric Dehais ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Mental Workload ◽  
Flight Simulator ◽  
Traffic Pattern ◽  
Assessment Techniques

Distributed Denial of Service (DDoS) detection by traffic pattern analysis

2012 ◽  
Vol 7 (4) ◽  
pp. 346-358 ◽  
Author(s):  
Theerasak Thapngam ◽  
Shui Yu ◽  
Wanlei Zhou ◽  
S. Kami Makki
Keyword(s):  
Pattern Analysis ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
Traffic Pattern ◽  
Ddos Detection

scholarly journals Real-time Traffic Pattern Analysis and Inference with Sparse Video Surveillance Information

2018 ◽  
Author(s):  
Yang Wang ◽  
Yiwei Xiao ◽  
Xike Xie ◽  
Ruoyu Chen ◽  
Hengchang Liu
Keyword(s):  
Real Time ◽  
Video Surveillance ◽  
Pattern Analysis ◽  
Transition Probabilities ◽  
Urban Traffic ◽  
Third Party ◽  
Distribution Model ◽  
Traffic Patterns ◽  
Traffic Pattern ◽  
Real Time Traffic

Recent advances in  video surveillance systems enable a new paradigm for intelligent urban traffic management systems. Since surveillance cameras are usually sparsely located to cover key regions of the road under surveillance, it is a big challenge to perform a complete real-time traffic pattern analysis based on incomplete sparse surveillance information. As a result, existing works mostly focus on predicting traffic volumes with historical records available at a particular location  and may not provide a complete picture of real-time traffic patterns. To this end, in this paper, we go beyond existing works and tackle the challenges of traffic flow analysis from three perspectives. First, we train the transition probabilities to capture vehicles' movement patterns. The transition probabilities are trained from third-party vehicle GPS data, and thus can work in the area even if there is no camera. Second, we exploit the Multivariate Normal Distribution model together with the transferred probabilities to estimate the unobserved traffic patterns. Third, we propose an algorithm for real-time traffic inference with  surveillance as a complement source of information. Finally, experiments on real-world data show the effectiveness of our approach.

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