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| 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) | |