Name
Title | ||
|---|---|---|
A vision-based abnormal trajectory detection framework for online traffic incident alert on freeways |
Abstract | ||
|---|---|---|
Abnormal trajectory detection from surveillance cameras is a highly desirable but challenging task, especially for online traffic incident alert on freeways. Existing methods are mainly customized for offline alert and easily suffer from false alerts when applying them to online alert. To fill this gap, an anomaly trajectory detection framework is proposed for online traffic incident alert on freeways. Based on a LSTM autoencoder, this framework introduces an adversarial learner (AL) for offline training and an abnormal trajectory discriminator (ATD) for online alert. The adversarial learner uses an additional adversarial loss to enable the autoencoder to learn a better normal trajectory pattern that is beneficial for reducing false alerts, while an abnormal trajectory discriminator is established and trained to detect small mean shift and filter out instantaneous false alerts. The experimental results show that our proposed framework effectively filters out false alerts and obtains a state-of-art performance (AUC = 0.97) compared to existing methods. Moreover, our framework could timely alert traffic incidents within 0.25 s, which is significant for timely preventing the occurrence of traffic crashes and improving the response speed of incident management on freeways. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1007/s00521-022-07335-w | Neural Computing and Applications |
Keywords | DocType | Volume |
Abnormal trajectory detection, Online traffic incident detection, Adversarial learner, Abnormal trajectory discriminator | Journal | 34 |
Issue | ISSN | Citations |
17 | 0941-0643 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhou Wei | 1 | 24 | 22.00 |
| Yunhong Yu | 2 | 0 | 0.34 |
| Yunfei Zhan | 3 | 0 | 0.34 |
| Chen Wang | 4 | 3 | 9.53 |