---
title: Mot Metrics
emoji: 📚
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:
```python
>>> 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
```bibtex
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}}
```
    
```bibtex
@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}}
```

## Further References
- [Github Repository - py-motmetrics](https://github.com/cheind/py-motmetrics/tree/develop)