Comments (5)
it is explained in this paper: MOT16: A Benchmark for Multi-Object Tracking
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@WurmD did you find an answer to this issue? I am also wondering about the same thing. What do the 9 columns represent exactlt? I am not sure about the last 3 fields.
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To summarize the paper, the columns are as follows:
<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <class>, <visibility>
where visibility is 1 if the object is completely visible and 0 if the object is completely occluded, and classes are:
Pedestrian 1
Person on vehicle 2
Car 3
Bicycle 4
Motorbike 5
Non motorized vehicle 6
Static person 7
Distractor 8
Occluder 9
Occluder on the ground 10
Occluder full 11
Reflection 12
Only ground-truth pedestrians are included in scoring
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scoring
, I would like to ask you,when i make my own data annotations , how is the visibility rate mentioned in the annotation obtained?
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Not 100% sure, try the paper. I'm guessing they combine truncation (how much of the object is within the frame) and intersection with other objects, possibly taking into account which objects are in front of one another.
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Related Issues (16)
- How to customize this code to evaluate MOT metrics on custom datasets? HOT 2
- ground truth for the images in the test folder of MOT16 HOT 1
- 3DZeF or 3DZeF20 HOT 1
- MOT20Det Evaluation Clarification
- MOT 20 Data annotation HOT 5
- HT21 annotation and det eval
- custom data annotation?
- Segmentation GT masks for MOT-17 OR Tracking GT annotations for MOTS
- Pose keypoints annotations for MOTSynth
- open source HOT 1
- What does it mean for confidence of detections to be zero in MOT20\train\MOT20-01\det\det.txt? HOT 1
- MinCostMatching headers HOT 1
- Hungarian.m returns incorrect solution in presence of infinite edges HOT 2
- MinCostMatching may return incorrect result with infinite edges
- MATLAB evalutation failed
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