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metadata
title: Mot Metrics
emoji: π
colorFrom: gray
colorTo: green
tags:
- evaluate
- metric
description: 'TODO: add a description here'
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
How to Use
The MOT metrics takes two numeric arrays as input corresponding to the predictions and references bounding boxes:
>>> import numpy as np
>>> module = evaluate.load("SEA-AI/mot-metrics")
>>> predicted =[[1,1,10,20,30,40,0.85],[2,1,15,25,35,45,0.78],[2,2,55,65,75,85,0.95]]
>>> ground_truth = [[1, 1, 10, 20, 30, 40],[2, 1, 15, 25, 35, 45]]
>>> results = module._compute(predictions=predicted, references=ground_truth, max_iou=0.5)
>>> results
{'idf1': 0.8421052631578947, 'idp': 0.8888888888888888,
'idr': 0.8, 'recall': 0.8, 'precision': 0.8888888888888888,
'num_unique_objects': 3,'mostly_tracked': 2,
'partially_tracked': 1, 'mostly_lost': 0,
'num_false_positives': 1, 'num_misses': 2,
'num_switches': 0, 'num_fragmentations': 0,
'mota': 0.7, 'motp': 0.02981870229007634,
'num_transfer': 0, 'num_ascend': 0,
'num_migrate': 0}
Input
Each line of the predictions array is a list with the following format:
[frame ID, object ID, x, y, width, height, confidence]
Each line of the references array is a list with the following format:
[frame ID, object ID, x, y, width, height]
The max_iou
parameter is used to filter out the bounding boxes with IOU less than the threshold. The default value is 0.5. This means that if a ground truth and a predicted bounding boxes IoU value is less than 0.5, then the predicted bounding box is not considered for association.
Output
The output is a dictionary containing the following metrics:
Name | Description |
---|---|
idf1 | ID measures: global min-cost F1 score. |
idp | ID measures: global min-cost precision. |
idr | ID measures: global min-cost recall. |
recall | Number of detections over number of objects. |
precision | Number of detected objects over sum of detected and false positives. |
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_false_positives | Total number of false positives (false-alarms). |
num_misses | Total number of misses. |
num_switches | Total number of track switches. |
num_fragmentations | Total number of switches from tracked to not tracked. |
mota | Multiple object tracker accuracy. |
motp | Multiple object tracker precision. |
Citations
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}}
@article{milan2016mot16,
title={MOT16: A benchmark for multi-object tracking},
author={Milan, Anton and Leal-Taix{\'e}, Laura and Reid, Ian and Roth, Stefan and Schindler, Konrad},
journal={arXiv preprint arXiv:1603.00831},
year={2016}}