In crowded waterways, maritime traffic is bound to speed regulations for safety reasons. Although several speed measurement techniques exist for road traffic, no known systems are available for maritime traffic. In this paper, we introduce a novel vessel speed enforcement system, based on visual detection and reidentification between two cameras along a waterway. We introduce a newly captured Vessel-reID dataset, containing 2,474 unique vessels. Our vessel detector is based on the Single Shot Multibox Detector and localizes vessels in each camera individually. Our re-identification algorithm, based on the TriNet model, matches vessels between the cameras. In general, vessels are detected over a large range of their in-view trajectory (over 92% and 95%, for Camera 1 and 2, respectively), which makes the re-identification experiments reliable. For re-identification, application specific techniques, i.e. trajectory matching and time filtering, improve our baseline re-identification model (49:5% mAP) with over 20% mAP. In the final evaluation, we show that 77% (Rank-1 score) of the vessels are correctly re-identified in the other camera. This final result presents a feasible score for our novel vessel re-identification application. Moreover, our result could be further improved, as we have tested on new unseen data during other weather conditions.
|Number of pages||10|
|Publication status||Published - 2020|
|Event||15th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP2020) - Valetta, Malta|
Duration: 27 Feb 2020 → 29 Feb 2020
|Conference||15th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP2020)|
|Abbreviated title||VISAPP 2020|
|Period||27/02/20 → 29/02/20|
- Maritime Traffic Management
- Speed Enforcement Application
- Vessel Detection
- Vessel Re-identification
Groot, H. G. J., Zwemer, M. H., Wijnhoven, R. G. J., Bondarau, E., & de With, P. H. N. (2020). Vessel-speed enforcement system by multi-camera detection and re-identification. 268-277. Paper presented at 15th International Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP2020), Valetta, Malta.