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deepstream-MOTA

how to use

python main.py --GT_path elementary_school.csv --data_path data/ele/ --remove_percent 0

--GT_path: the path of ground truth

Position Name Description
1 Frame number Indicate at which frame the object is present
2 Identity number Each pedestrian trajectory is identified by a unique ID (−1 for detections)
3 Bounding box left Coordinate of the top-left corner of the pedestrian bounding box
4 Bounding box top Coordinate of the top-left corner of the pedestrian bounding box
5 Bounding box width Width in pixels of the pedestrian bounding box
6 Bounding box height Height in pixels of the pedestrian bounding box
7 Confidence score Indicates how confident the detector is that this instance is a pedestrian. For the ground truth and results, it acts as a flag whether the entry is to be considered.
8 x 3D x position of the pedestrian in real-world coordinates (−1 if not available)
9 y 3D y position of the pedestrian in real-world coordinates (−1 if not available)
10 z 3D z position of the pedestrian in real-world coordinates (−1 if not available)

--data_path: the path of deepstream output data
("%s %lu 0.0 0 0.0 %f %f %f %f 0.0 0.0 0.0 0.0 0.0 0.0 0.0 %f\n", obj->obj_label, id, left, top, right, bottom, confidence)

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Evaluate DeepStream Trackers on KITTI

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