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TrackEval 代码逻辑，文件结构详解 / MOT Challenge 评测指标

2022-04-10 10:02:29 作者：互联网

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.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.py to 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
}

TrackEval has 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

Eval Sequence

Init evaluator = Evaluator in eval.py
- evaluator.evaluate()
evaluate_sequence in eval.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?

code details

# 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
标签：gt,res,Challenge,ids,MOT,tracker,TrackEval,np,data
来源： https://www.cnblogs.com/zxyfrank/p/16124826.html