gumballing / fvessel

FVessel: Benchmark Dataset for Vessel Detection, Tracking, and Data Fusion

FVessel: Benchmark Dataset for Vessel Detection, Tracking, and Data Fusion

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**Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways **
Yu Guo, Ryan Wen Liu, Jingxiang Qu, Yuxu Lu, Fenghua Zhu, Yisheng Lv
arXiv preprint arXiv:2302.11283

Introduction: The FVessel benchmark dataset is used to evaluate the reliability of AIS and video data fusion algorithms, which mainly contains 26 videos and the corresponding AIS data captured by the HIKVISION DS-2DC4423IW-D dome camera and Saiyang AIS9000-08 Class-B AIS receiver on the Wuhan Segment of the Yangtze River. To protect privacy, the MMSI for each vessel has been replaced with a random number in our dataset. As shown in Figure 1, these videos were captured under many locations (e.g., bridge region and riverside) and various weather conditions (e.g., sunny, cloudy, and low-light).

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Examples

Details

The FVessel dataset consists of two parts:

• 01_Video+AIS
• 02_Image+xml

01_Video+AIS

01_Video+AIS contains 26 videos and the corresponding AIS data to evaluate the performance of the data fusion algorithm.

Video	Video Length	Type	Weather	Times of Occlusions	Total Number of Vessels	Number of Vessels with AIS
video-01	10m07s	Bridge	Low-light	2	5	4
video-02	19m52s	Bridge	Sunny	6	7	6
video-03	19m14s	Riverside	Sunny	6	5	5
video-04	06m10s	Riverside	Sunny	0	1	1
video-05	15m01s	Riverside	Sunny	2	5	5
video-06	12m49s	Riverside	Sunny	2	4	4
video-07	03m38s	Riverside	Sunny	1	2	2
video-08	16m05s	Riverside	Sunny	3	6	5
video-09	05m25s	Riverside	Sunny	0	1	1
video-10	11m17s	Bridge	Cloudy	2	3	1
video-11	05m18s	Riverside	Sunny	1	3	3
video-12	07m19s	Riverside	Sunny	1	4	4
video-13	12m58s	Riverside	Sunny	5	6	6
video-14	03m58s	Riverside	Sunny	3	4	4
video-15	10m46s	Riverside	Sunny	0	4	4
video-16	05m05s	Riverside	Sunny	0	1	1
video-17	08m08s	Riverside	Sunny	1	2	2
video-18	23m57s	Riverside	Sunny	10	10	6
video-19	11m28s	Riverside	Low-light	0	2	2
video-20	14m10s	Riverside	Low-light	0	3	3
video-21	24m01s	Riverside	Low-light	4	7	6
video-22	02m40s	Riverside	Low-light	0	2	1
video-23	19m24s	Riverside	Sunny	2	4	4
video-24	08m39s	Riverside	Sunny	2	3	3
video-25	24m05s	Riverside	Sunny	4	8	8
video-26	07m26s	Riverside	Sunny	0	5	5

Each video data contains the following files:

• (a) AIS

  2022_05_10_19_21_04.csv

  Number	MMSI	Lon	Lat	Speed	Course	Heading	Type	Timestamp
  0	100000000	114.325327	30.60166	0	293.6	511	18	1652181559844
  1	130000000	114.302683	30.58059	6.8	33.6	33	18	1652181659157
  2	140000000	114.31004	30.599997	3.9	215.6	511	18	1652181655147
  3	600000000	114.3156	30.59773	7.2	39.6	511	18	1652181649704
  ...	...	...	...	...	...	...	...	...

  2022_05_10_19_21_05.csv
  2022_05_10_19_21_06.csv
  ...

  (Each csv file contains the AIS data received within ten minutes, and only the most recent data is kept.)

• (b) Video

  2022_05_10_19_21_05_19_31_10_b.mp4

  Starting time: 2022_05_10_19_21_05 End time: 2022_05_10_19_31_10 Type: b/r (bridge/riverside)

• (c) Camera Parameters

  camera_para.txt

  Lon	Lat	Horizontal Orientation	Vertical Orientation	Camera Height	Horizontal FoV	Vertical FoV	fx	fy	u0	v0
  114.32583	30.60139	7	-1	20	55	30.94	2391.26	2446.89	1305.04	855.214

  fx, fy, u0, and v0 are parameters in the internal matrix of the camera.

• (d) GT (Ground Truth: adopt the multi-object tracking MOT format)

  Video-02_gt.mp4

  Video containing ground truth, processed only once per second.

  Video-02_gt_detection.txt

  <second>, <0>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>
  

  Video-02_gt_tracking.txt

  <second>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>
  

  Video-02_gt_fusion.txt

  <second>, <mmsi>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>
  

02_Image+xml

02_Image+xml contains 7625 maritime images and the corresponding xml files for target detection network training. This dataset has only one class vessel.

Download

Link：https://pan.baidu.com/s/19R1qSq0xTBmER8tj9WcRGA

Extraction code：MIPC

AIS Data Coordinate Transformation

• Copy the data into the 01_demo_transform/data of this project.

• Run 01_demo_transformc/main.py.

Example for Coordinate Transformation

Link：https://pan.baidu.com/s/1VBQc0QVNdOLGkpmqVZKSHg

Extraction code：mipc

Note that the AIS data in the example has been processed differently from the AIS data in the FVessel dataset.

video.mp4

(The blue line is the projection of the AIS data-based trajectory in the image, and the red letter is the corresponding mmsi number.)

Evaluation

• Install motmetrics.

• Copy the two files from the 02_demo_metric/motmetrics of this project to the installed motmetrics folder.

• Save the test files to the 02_demo_metric/sample folder.

• Choose the type of evaluation detection, tracking, and fusion.

• Run 02_demo_metric/eval.py.

DeepSORVF: Deep Learning-based Simple Online and Real-Time Vessel Data Fusion Method

The following videos show the data fusion results of our proposed DeepSORVF.

1_5.mp4

Acknowledgements

We deeply thank Jianlong Su from the School of Computer and Artificial Intelligence in Wuhan University of Technology who performs the data acquisition and algorithm implementation works.

Citation

@article{guo2023asynchronous,
  title={Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion for Vessel Traffic Surveillance in Inland Waterways},
  author={Guo, Yu and Liu, Ryan Wen and Qu, Jingxiang and Lu, Yuxu and Zhu, Fenghua, and Lv, Yisheng},
  journal={arXiv preprint arXiv:2302.11283},
  year={2023}
}

If you have any questions, please get in touch with me ([email protected] & [email protected]).