Data format
- Note: Training Test data in https://motchallenge.net/ is not the required(default) format of TrackEval
MOT Challenge train/val/test
det.txt
3,-1,1433,512,60,100,0,-1,-1,-1
3,-1,1048,437,49,124,0,-1,-1,-1
3,-1,1087,552,78,177,0,-1,-1,-1
3,-1,1504,514,51,101,0,-1,-1,-1
gt.txt
48,1,335,811,128,270,1,1,0.85675
49,1,335,809,130,272,1,1,0.85326
50,1,335,808,132,272,1,1,0.85579
img1/
f”{:06d}.jpg”
000001.jpg
000002.jpg
000003.jpg
seqinfo.ini
[Sequence]
name=MOT20-01
imDir=img1
frameRate=25a
seqLength=429
imWidth=1920
imHeight=1080
imExt=.jpg
TrackEval
<frame>, <id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <conf>, <x>, <y>, <z>
All frame numbers, target IDs and bounding boxes are 1-based.
Here is an example from the sample data offered by TrackEval
1,6.0,343.8669738769531,828.7033081054688,124.1097412109375,248.24200439453125,1,-1,-1,-1
1,7.0,1023.822265625,606.1856689453125,83.2244873046875,195.18109130859372,1,-1,-1,-1
1,8.0,1067.532958984375,513.0377197265625,52.221435546875,142.29217529296875,1,-1,-1,-1
Folder Hierarchy
-
gt
-
gt.txt
txt details
11,1,227,812,140,269,1,1,0.83704 12,1,230,811,137,270,1,1,0.83764 13,1,233,810,135,271,1,1,0.83456 14,1,236,809,133,272,1,1,0.8315 15,1,239,808,131,273,1,1,0.82847 -
seqinfo.ini(the same as MOT17 training data)txt details
[Sequence] name=MOT20-01 imDir=img1 frameRate=25 seqLength=429 imWidth=1920 imHeight=1080 imExt=.jpg
-
-
trackers
refer to default_dataset_config = trackeval.datasets.MotChallenge2DBox.get_default_dataset_config() in run_mot_challenge.py
"GT_FOLDER": "/path/to/TrackEval/data/gt/mot_challenge/",
"TRACKERS_FOLDER": "/path/to/TrackEval/data/trackers/mot_challenge/",
"OUTPUT_FOLDER": None,
"TRACKERS_TO_EVAL": [
"MPNTrack"
],
"TRACKER_SUB_FOLDER": "data",
"OUTPUT_SUB_FOLDER": "",
curr_file = os.path.join(self.tracker_fol, tracker,
self.tracker_sub_fol, seq + '.txt')
refer to
mot_challenge_2d_box.pyto see how it get txt path
Pipeline
Data Preperation & arguments
GT_FOLDER: sequence with ground truth- in the format of MOT17
- refer to
mot_challenge_2d_box.py
`default_dataset_config` details
code_path = utils.get_code_path()
'GT_FOLDER': os.path.join(code_path, 'data/gt/mot_challenge/'), # Location of GT data
'TRACKERS_FOLDER': os.path.join(code_path, 'data/trackers/mot_challenge/'), # Trackers location
`dataset_config` details
{
"PRINT_CONFIG": True,
"GT_FOLDER": "/path/to/TrackEval/data/gt/mot_challenge/",
"TRACKERS_FOLDER": "/path/to/TrackEval/data/trackers/mot_challenge/",
"OUTPUT_FOLDER": None,
"TRACKERS_TO_EVAL": [
"MPNTrack"
],
"CLASSES_TO_EVAL": [
"pedestrian"
],
"BENCHMARK": "MOT17",
"SPLIT_TO_EVAL": "train",
"INPUT_AS_ZIP": False,
"DO_PREPROC": True,
"TRACKER_SUB_FOLDER": "data",
"OUTPUT_SUB_FOLDER": "",
"TRACKER_DISPLAY_NAMES": None,
"SEQMAP_FOLDER": None, ...
}
`eval_config` details
{
"USE_PARALLEL": False,
"NUM_PARALLEL_CORES": 1,
"BREAK_ON_ERROR": True,
"RETURN_ON_ERROR": False,
"LOG_ON_ERROR": "/path/to/TrackEval/error_log.txt",
"PRINT_RESULTS": True,
"PRINT_ONLY_COMBINED": False,
"PRINT_CONFIG": True,
"TIME_PROGRESS": True,
"DISPLAY_LESS_PROGRESS": False,
"OUTPUT_SUMMARY": True,
"OUTPUT_EMPTY_CLASSES": True,
"OUTPUT_DETAILED": True,
"PLOT_CURVES": True
}
`metrics_config` details
{
"METRICS": [
"HOTA",
"CLEAR",
"Identity",
"VACE"
],
"THRESHOLD": 0.5
}
TrackEvalhas its own list arguments parser(command line to Python’s Argument Parser)
Init Dataset
_get_seq_info
gt_set = self.config['BENCHMARK'] + '-' + self.config['SPLIT_TO_EVAL']
self.gt_set = gt_set
if self.config["SEQMAP_FOLDER"] is None:
seqmap_file = os.path.join(self.config['GT_FOLDER'], 'seqmaps', self.gt_set + '.txt')
seqmap
name
TUD-Stadtmitte
TUD-Campus
PETS09-S2L1
ETH-Bahnhof
ETH-Sunnyday
ETH-Pedcross2
ADL-Rundle-6
ADL-Rundle-8
KITTI-13
KITTI-17
Venice-2
Init Evaluator
- Init
evaluator = Evaluatorineval.pyevaluator.evaluate()
evaluate_sequenceineval.py
raw_data = dataset.get_raw_seq_data(tracker, seq)
seq_res = {}
for cls in class_list:
seq_res[cls] = {}
data = dataset.get_preprocessed_seq_data(raw_data, cls)
for metric, met_name in zip(metrics_list, metric_names):
seq_res[cls][met_name] = metric.eval_sequence(data)
return seq_res
Load Data
Data are loaded in a dict. Key is frame_id(timestep), and the value is the splitted row.
['21', '1', '912', '484', '97', '109', '0', '7', '1']
convert to ndarray
time_data = np.asarray(read_data[time_key], dtype=np.float)
raw_data['dets'][t] = np.atleast_2d(time_data[:, 2:6])
raw_data['ids'][t] = np.atleast_1d(time_data[:, 1]).astype(int)
Calculate IoU Similarity
MotChallenge2DBox._calculate_similarities -> MotChallenge2DBox._calculate_box_ious
similarity_scores = []
for t, (gt_dets_t, tracker_dets_t) in enumerate(zip(raw_data['gt_dets'], raw_data['tracker_dets'])):
ious = self._calculate_similarities(gt_dets_t, tracker_dets_t)
similarity_scores.append(ious)
raw_data['similarity_scores'] = similarity_scores
How to Calculate IoU?
# layout: (x0, y0, x1, y1) min_ = np.minimum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :]) max_ = np.maximum(bboxes1[:, np.newaxis, :], bboxes2[np.newaxis, :, :]) intersection = np.maximum(min_[..., 2] - max_[..., 0], 0) * np.maximum(min_[..., 3] - max_[..., 1], 0) area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (bboxes1[..., 3] - bboxes1[..., 1]) area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (bboxes2[..., 3] - bboxes2[..., 1]) union = area1[:, np.newaxis] + area2[np.newaxis, :] - intersection intersection[area1 <= 0 + np.finfo('float').eps, :] = 0 intersection[:, area2 <= 0 + np.finfo('float').eps] = 0 intersection[union <= 0 + np.finfo('float').eps] = 0 union[union <= 0 + np.finfo('float').eps] = 1 ious = intersection / union
get_preprocessed_seq_data in mot_challenge_2d_box.py
Cancel the distractor
matching_scores[matching_scores < 0.5 - np.finfo('float').eps] = 0
match_rows, match_cols = linear_sum_assignment(-matching_scores)
[Note
原创文章，作者：kirin，如若转载，请注明出处：https://blog.ytso.com/245435.html