The py-motmetrics library provides a Python implementation of metrics for benchmarking multiple object trackers (MOT).
While benchmarking single object trackers is rather straightforward, measuring the performance of multiple object trackers needs careful design as multiple correspondence constellations can arise (see image below). A variety of methods have been proposed in the past and while there is no general agreement on a single method, the methods of [1,2,3,4] have received considerable attention in recent years. py-motmetrics implements these metrics.
Pictures courtesy of Bernardin, Keni, and Rainer Stiefelhagen [1]
In particular py-motmetrics supports CLEAR-MOT[1,2] metrics and ID[4] metrics. Both metrics attempt to find a minimum cost assignment between ground truth objects and predictions. However, while CLEAR-MOT solves the assignment problem on a local per-frame basis, ID-MEASURE solves the bipartite graph matching by finding the minimum cost of objects and predictions over all frames. This blog-post by Ergys illustrates the differences in more detail.
- Variety of metrics
Provides MOTA, MOTP, track quality measures, global ID measures and more. The results are comparable with the popular MOTChallenge benchmarks (*1). - Distance agnostic
Supports Euclidean, Intersection over Union and other distances measures. - Complete event history
Tracks all relevant per-frame events suchs as correspondences, misses, false alarms and switches. - Flexible solver backend
Support for switching minimum assignment cost solvers. Supportsscipy,ortools,munkresout of the box. Auto-tunes solver selection based on availability and problem size. - Easy to extend
Events and summaries are utilizing pandas for data structures and analysis. New metrics can reuse already computed values from depending metrics.
py-motmetrics implements the following metrics. The metrics have been aligned with what is reported by MOTChallenge benchmarks.
import motmetrics as mm
# List all default metrics
mh = mm.metrics.create()
print(mh.list_metrics_markdown())| Name | Description |
|---|---|
| num_frames | Total number of frames. |
| num_matches | Total number matches. |
| num_switches | Total number of track switches. |
| num_false_positives | Total number of false positives (false-alarms). |
| num_misses | Total number of misses. |
| num_detections | Total number of detected objects including matches and switches. |
| num_objects | Total number of unique object appearances over all frames. |
| num_predictions | Total number of unique prediction appearances over all frames. |
| num_unique_objects | Total number of unique object ids encountered. |
| mostly_tracked | Number of objects tracked for at least 80 percent of lifespan. |
| partially_tracked | Number of objects tracked between 20 and 80 percent of lifespan. |
| mostly_lost | Number of objects tracked less than 20 percent of lifespan. |
| num_fragmentations | Total number of switches from tracked to not tracked. |
| motp | Multiple object tracker precision. |
| mota | Multiple object tracker accuracy. |
| precision | Number of detected objects over sum of detected and false positives. |
| recall | Number of detections over number of objects. |
| idfp | ID measures: Number of false positive matches after global min-cost matching. |
| idfn | ID measures: Number of false negatives matches after global min-cost matching. |
| idtp | ID measures: Number of true positives matches after global min-cost matching. |
| idp | ID measures: global min-cost precision. |
| idr | ID measures: global min-cost recall. |
| idf1 | ID measures: global min-cost F1 score. |
| obj_frequencies | pd.Series Total number of occurrences of individual objects over all frames. |
| pred_frequencies | pd.Series Total number of occurrences of individual predictions over all frames. |
| track_ratios | pd.Series Ratio of assigned to total appearance count per unique object id. |
| id_global_assignment | dict ID measures: Global min-cost assignment for ID measures. |
| deta_alpha | HOTA: Detection Accuracy (DetA) for a given threshold. |
| assa_alpha | HOTA: Association Accuracy (AssA) for a given threshold. |
| hota_alpha | HOTA: Higher Order Tracking Accuracy (HOTA) for a given threshold. |
py-motmetrics produces results compatible with popular MOTChallenge benchmarks (*1). Below are two results taken from MOTChallenge Matlab devkit corresponding to the results of the CEM tracker on the training set of the 2015 MOT 2DMark.
TUD-Campus
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
55.8 73.0 45.1| 58.2 94.1 0.18| 8 1 6 1| 13 150 7 7| 52.6 72.3 54.3
TUD-Stadtmitte
IDF1 IDP IDR| Rcll Prcn FAR| GT MT PT ML| FP FN IDs FM| MOTA MOTP MOTAL
64.5 82.0 53.1| 60.9 94.0 0.25| 10 5 4 1| 45 452 7 6| 56.4 65.4 56.9
In comparison to py-motmetrics
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP
TUD-Campus 55.8% 73.0% 45.1% 58.2% 94.1% 8 1 6 1 13 150 7 7 52.6% 0.277
TUD-Stadtmitte 64.5% 82.0% 53.1% 60.9% 94.0% 10 5 4 1 45 452 7 6 56.4% 0.346
(*1) Besides naming conventions, the only obvious differences are
- Metric
FARis missing. This metric is given implicitly and can be recovered byFalsePos / Frames * 100. - Metric
MOTPseems to be off. To convert compute(1 - MOTP) * 100. MOTChallenge benchmarks computeMOTPas percentage, while py-motmetrics sticks to the original definition of average distance over number of assigned objects [1].
You can compare tracker results to ground truth in MOTChallenge format by
python -m motmetrics.apps.eval_motchallenge --help
For MOT16/17, you can run
python -m motmetrics.apps.evaluateTracking --help
To install latest development version of py-motmetrics (usually a bit more recent than PyPi below)
pip install git+https://github.com/cheind/py-motmetrics.git
To install py-motmetrics use pip
pip install motmetrics
Python 3.5/3.6/3.9 and numpy, pandas and scipy is required. If no binary packages are available for your platform and building source packages fails, you might want to try a distribution like Conda (see below) to install dependencies.
Alternatively for developing, clone or fork this repository and install in editing mode.
pip install -e <path/to/setup.py>
In case you are using Conda, a simple way to run py-motmetrics is to create a virtual environment with all the necessary dependencies
conda env create -f environment.yml
> activate motmetrics-env
Then activate / source the motmetrics-env and install py-motmetrics and run the tests.
activate motmetrics-env
pip install .
pytest
In case you already have an environment you install the dependencies from within your environment by
conda install --file requirements.txt
pip install .
pytest
import motmetrics as mm
import numpy as np
# Create an accumulator that will be updated during each frame
acc = mm.MOTAccumulator(auto_id=True)
# Call update once for per frame. For now, assume distances between
# frame objects / hypotheses are given.
acc.update(
[1, 2], # Ground truth objects in this frame
[1, 2, 3], # Detector hypotheses in this frame
[
[0.1, np.nan, 0.3], # Distances from object 1 to hypotheses 1, 2, 3
[0.5, 0.2, 0.3] # Distances from object 2 to hypotheses 1, 2, 3
]
)The code above updates an event accumulator with data from a single frame. Here we assume that pairwise object / hypothesis distances have already been computed. Note np.nan inside the distance matrix. It signals that object 1 cannot be paired with hypothesis 2. To inspect the current event history simple print the events associated with the accumulator.
print(acc.events) # a pandas DataFrame containing all events
"""
Type OId HId D
FrameId Event
0 0 RAW 1 1 0.1
1 RAW 1 2 NaN
2 RAW 1 3 0.3
3 RAW 2 1 0.5
4 RAW 2 2 0.2
5 RAW 2 3 0.3
6 MATCH 1 1 0.1
7 MATCH 2 2 0.2
8 FP NaN 3 NaN
"""The above data frame contains RAW and MOT events. To obtain just MOT events type
print(acc.mot_events) # a pandas DataFrame containing MOT only events
"""
Type OId HId D
FrameId Event
0 6 MATCH 1 1 0.1
7 MATCH 2 2 0.2
8 FP NaN 3 NaN
"""Meaning object 1 was matched to hypothesis 1 with distance 0.1. Similarily, object 2 was matched to hypothesis 2 with distance 0.2. Hypothesis 3 could not be matched to any remaining object and generated a false positive (FP). Possible assignments are computed by minimizing the total assignment distance (Kuhn-Munkres algorithm).
Continuing from above
frameid = acc.update(
[1, 2],
[1],
[
[0.2],
[0.4]
]
)
print(acc.mot_events.loc[frameid])
"""
Type OId HId D
Event
2 MATCH 1 1 0.2
3 MISS 2 NaN NaN
"""While object 1 was matched, object 2 couldn't be matched because no hypotheses are left to pair with.
frameid = acc.update(
[1, 2],
[1, 3],
[
[0.6, 0.2],
[0.1, 0.6]
]
)
print(acc.mot_events.loc[frameid])
"""
Type OId HId D
Event
4 MATCH 1 1 0.6
5 SWITCH 2 3 0.6
"""Object 2 is now tracked by hypothesis 3 leading to a track switch. Note, although a pairing (1, 3) with cost less than 0.6 is possible, the algorithm prefers prefers to continue track assignments from past frames which is a property of MOT metrics.
Once the accumulator has been populated you can compute and display metrics. Continuing the example from above
mh = mm.metrics.create()
summary = mh.compute(acc, metrics=['num_frames', 'mota', 'motp'], name='acc')
print(summary)
"""
num_frames mota motp
acc 3 0.5 0.34
"""Computing metrics for multiple accumulators or accumulator views is also possible
summary = mh.compute_many(
[acc, acc.events.loc[0:1]],
metrics=['num_frames', 'mota', 'motp'],
names=['full', 'part'])
print(summary)
"""
num_frames mota motp
full 3 0.5 0.340000
part 2 0.5 0.166667
"""Finally, you may want to reformat column names and how column values are displayed.
strsummary = mm.io.render_summary(
summary,
formatters={'mota' : '{:.2%}'.format},
namemap={'mota': 'MOTA', 'motp' : 'MOTP'}
)
print(strsummary)
"""
num_frames MOTA MOTP
full 3 50.00% 0.340000
part 2 50.00% 0.166667
"""For MOTChallenge py-motmetrics provides predefined metric selectors, formatters and metric names, so that the result looks alike what is provided via their Matlab devkit.
summary = mh.compute_many(
[acc, acc.events.loc[0:1]],
metrics=mm.metrics.motchallenge_metrics,
names=['full', 'part'])
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
print(strsummary)
"""
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP
full 83.3% 83.3% 83.3% 83.3% 83.3% 2 1 1 0 1 1 1 1 50.0% 0.340
part 75.0% 75.0% 75.0% 75.0% 75.0% 2 1 1 0 1 1 0 0 50.0% 0.167
"""In order to generate an overall summary that computes the metrics jointly over all accumulators add generate_overall=True as follows
summary = mh.compute_many(
[acc, acc.events.loc[0:1]],
metrics=mm.metrics.motchallenge_metrics,
names=['full', 'part'],
generate_overall=True
)
strsummary = mm.io.render_summary(
summary,
formatters=mh.formatters,
namemap=mm.io.motchallenge_metric_names
)
print(strsummary)
"""
IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDs FM MOTA MOTP
full 83.3% 83.3% 83.3% 83.3% 83.3% 2 1 1 0 1 1 1 1 50.0% 0.340
part 75.0% 75.0% 75.0% 75.0% 75.0% 2 1 1 0 1 1 0 0 50.0% 0.167
OVERALL 80.0% 80.0% 80.0% 80.0% 80.0% 4 2 2 0 2 2 1 1 50.0% 0.275
"""Computing HOTA metrics is also possible. However, it cannot be used with the Accumulator class directly, as HOTA requires to computing a reweighting matrix from all the frames at the beginning. Here is an example of how to use it:
import os
import numpy as np
import motmetrics as mm
def compute_motchallenge(dir_name):
# `gt.txt` and `test.txt` should be prepared in MOT15 format
df_gt = mm.io.loadtxt(os.path.join(dir_name, "gt.txt"))
df_test = mm.io.loadtxt(os.path.join(dir_name, "test.txt"))
# Require different thresholds for matching
th_list = np.arange(0.05, 0.99, 0.05)
res_list = mm.utils.compare_to_groundtruth_reweighting(df_gt, df_test, "iou", distth=th_list)
return res_list
# `data_dir` is the directory containing the gt.txt and test.txt files
acc = compute_motchallenge("data_dir")
mh = mm.metrics.create()
summary = mh.compute_many(
acc,
metrics=[
"deta_alpha",
"assa_alpha",
"hota_alpha",
],
generate_overall=True, # `Overall` is the average we need only
)
strsummary = mm.io.render_summary(
summary.iloc[[-1], :], # Use list to preserve `DataFrame` type
formatters=mh.formatters,
namemap={"hota_alpha": "HOTA", "assa_alpha": "ASSA", "deta_alpha": "DETA"},
)
print(strsummary)
"""
# data_dir=motmetrics/data/TUD-Campus
DETA ASSA HOTA
OVERALL 41.8% 36.9% 39.1%
# data_dir=motmetrics/data/TUD-Stadtmitte
DETA ASSA HOTA
OVERALL 39.2% 40.9% 39.8%
"""Up until this point we assumed the pairwise object/hypothesis distances to be known. Usually this is not the case. You are mostly given either rectangles or points (centroids) of related objects. To compute a distance matrix from them you can use motmetrics.distance module as shown below.
# Object related points
o = np.array([
[1., 2],
[2., 2],
[3., 2],
])
# Hypothesis related points
h = np.array([
[0., 0],
[1., 1],
])
C = mm.distances.norm2squared_matrix(o, h, max_d2=5.)
"""
[[ 5. 1.]
[ nan 2.]
[ nan 5.]]
"""a = np.array([
[0, 0, 1, 2], # Format X, Y, Width, Height
[0, 0, 0.8, 1.5],
])
b = np.array([
[0, 0, 1, 2],
[0, 0, 1, 1],
[0.1, 0.2, 2, 2],
])
mm.distances.iou_matrix(a, b, max_iou=0.5)
"""
[[ 0. 0.5 nan]
[ 0.4 0.42857143 nan]]
"""For large datasets solving the minimum cost assignment becomes the dominant runtime part. py-motmetrics therefore supports these solvers out of the box
lapsolver- https://github.com/cheind/py-lapsolverlapjv- https://github.com/gatagat/lapscipy- https://github.com/scipy/scipy/tree/master/scipyortools<9.4- https://github.com/google/or-toolsmunkres- http://software.clapper.org/munkres/
A comparison for different sized matrices is shown below (taken from here)
Please note that the x-axis is scaled logarithmically. Missing bars indicate excessive runtime or errors in returned result.
By default py-motmetrics will try to find a LAP solver in the order of the list above. In order to temporarly replace the default solver use
costs = ...
mysolver = lambda x: ... # solver code that returns pairings
with lap.set_default_solver(mysolver):
...Use this section as a guide for calculating MOT metrics for your custom dataset.
Before you begin, make sure to have Ground truth and your Tracker output data in the form of text files. The code below assumes MOT16 format for the ground truth as well as the tracker ouput. The data is arranged in the following sequence:
<frame number>, <object id>, <bb_left>, <bb_top>, <bb_width>, <bb_height>, <confidence>, <x>, <y>, <z>
A sample ground truth/tracker output file is shown below. If you are using a custom dataset, then it is highly likely that you will have to create your own ground truth file. If you already have a MOT16 format ground truth file, you can use it directly otherwise, you will need a MOT16 annotator tool to create the annotations (ground truth). You can use any tool to create your ground truth data, just make sure it is as per MOT16 format.
If you can't find a tool to create your ground truth files, you can use this free MOT16 annotator tool to create ground truth for your dataset which can then be used in conjunction with your tracker output to generate the MOT metrics.
1,1,763.00,272.00,189.00,38.00,1,-1,-1,-1
1,2,412.00,265.00,153.00,30.00,1,-1,-1,-1
2,1,762.00,269.00,185.00,41.00,1,-1,-1,-1
2,2,413.00,267.00,151.00,26.00,1,-1,-1,-1
3,1,760.00,272.00,186.00,38.00,1,-1,-1,-1
You can read more about MOT16 format here.
Following function loads the ground truth and tracker object files, processes them and produces a set of metrices.
def motMetricsEnhancedCalculator(gtSource, tSource):
# import required packages
import motmetrics as mm
import numpy as np
# load ground truth
gt = np.loadtxt(gtSource, delimiter=',')
# load tracking output
t = np.loadtxt(tSource, delimiter=',')
# Create an accumulator that will be updated during each frame
acc = mm.MOTAccumulator(auto_id=True)
# Max frame number maybe different for gt and t files
for frame in range(int(gt[:,0].max())):
frame += 1 # detection and frame numbers begin at 1
# select id, x, y, width, height for current frame
# required format for distance calculation is X, Y, Width, Height \
# We already have this format
gt_dets = gt[gt[:,0]==frame,1:6] # select all detections in gt
t_dets = t[t[:,0]==frame,1:6] # select all detections in t
C = mm.distances.iou_matrix(gt_dets[:,1:], t_dets[:,1:], \
max_iou=0.5) # format: gt, t
# Call update once for per frame.
# format: gt object ids, t object ids, distance
acc.update(gt_dets[:,0].astype('int').tolist(), \
t_dets[:,0].astype('int').tolist(), C)
mh = mm.metrics.create()
summary = mh.compute(acc, metrics=['num_frames', 'idf1', 'idp', 'idr', \
'recall', 'precision', 'num_objects', \
'mostly_tracked', 'partially_tracked', \
'mostly_lost', 'num_false_positives', \
'num_misses', 'num_switches', \
'num_fragmentations', 'mota', 'motp' \
], \
name='acc')
strsummary = mm.io.render_summary(
summary,
#formatters={'mota' : '{:.2%}'.format},
namemap={'idf1': 'IDF1', 'idp': 'IDP', 'idr': 'IDR', 'recall': 'Rcll', \
'precision': 'Prcn', 'num_objects': 'GT', \
'mostly_tracked' : 'MT', 'partially_tracked': 'PT', \
'mostly_lost' : 'ML', 'num_false_positives': 'FP', \
'num_misses': 'FN', 'num_switches' : 'IDsw', \
'num_fragmentations' : 'FM', 'mota': 'MOTA', 'motp' : 'MOTP', \
}
)
print(strsummary)Run the function by pointing to the ground truth and tracker output file. A sample output is shown below.
# Calculate the MOT metrics
motMetricsEnhancedCalculator('gt/groundtruth.txt', \
'to/trackeroutput.txt')
"""
num_frames IDF1 IDP IDR Rcll Prcn GT MT PT ML FP FN IDsw FM MOTA MOTP
acc 150 0.75 0.857143 0.666667 0.743295 0.955665 261 0 2 0 9 67 1 12 0.704981 0.244387
"""py-motmetrics uses the pytest framework. To run the tests, simply cd into the source directly and run pytest.
- Bernardin, Keni, and Rainer Stiefelhagen. "Evaluating multiple object tracking performance: the CLEAR MOT metrics." EURASIP Journal on Image and Video Processing 2008.1 (2008): 1-10.
- Milan, Anton, et al. "Mot16: A benchmark for multi-object tracking." arXiv preprint arXiv:1603.00831 (2016).
- Li, Yuan, Chang Huang, and Ram Nevatia. "Learning to associate: Hybridboosted multi-target tracker for crowded scene." Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
- Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking. E. Ristani, F. Solera, R. S. Zou, R. Cucchiara and C. Tomasi. ECCV 2016 Workshop on Benchmarking Multi-Target Tracking.
/data/train directory should contain MOT 2D 2015 Ground Truth files. /data/test directory should contain your results.
You can check usage and directory listing at https://github.com/cheind/py-motmetrics/blob/master/motmetrics/apps/eval_motchallenge.py
docker build -t desired-image-name -f Dockerfile .
docker run desired-image-name
(credits to christosavg)
MIT License
Copyright (c) 2017-2022 Christoph Heindl
Copyright (c) 2018 Toka
Copyright (c) 2019-2022 Jack Valmadre
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