Name
Abstract | ||
|---|---|---|
This paper proposes a machine-learning technique to detect vehicle anomalies from data captured by automatic number plate recognition (ANPR) system. The proposed anomaly detection technique is specially engineered to exploit both spatial and temporal features of vehicles captured by ANPR system, so as to accurately detect anomaly vehicles. We extensively evaluated the proposed technique using a two-month long dataset collected by a real world ANRP system, which has more than three hundred cameras deployed in a big city of China. The evaluation results show that our technique can effectively detect vehicle anomalies from the huge amount of data collected by the ANPR system. More importantly, our technique significantly outperforms existing schemes especially when the data collected by the ANRP system are noisy due to poor weather condition. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/GLOCOM.2015.7417520 | IEEE Global Communications Conference |
Keywords | Field | DocType |
anomaly detects,data mining,sensors data management,intelligent transportation,trajectory analysis | Computer vision,Anomaly detection,Logic gate,Computer science,Feature extraction,Real-time computing,Exploit,Artificial intelligence,Weather condition,Trajectory | Conference |
ISSN | Citations | PageRank |
2334-0983 | 1 | 0.35 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuyan Sun | 1 | 10 | 3.60 |
| Hongsong Zhu | 2 | 93 | 20.11 |
| Yong Liao | 3 | 249 | 21.07 |
| Sun Limin | 4 | 467 | 65.09 |