{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"}],"enrichment":{"capability":"Computes multiple object tracker (MOT) evaluation metrics including MOTA, MOTP, and ID measures by matching ground truth objects to predictions across video frames.","skillfed_tags":["computer-vision","tracking-evaluation","benchmarking"],"use_cases":["Benchmark a custom multi-object tracker against MOTChallenge datasets to compare MOTA/MOTP scores","Evaluate pedestrian or vehicle detection and tracking pipelines in autonomous driving or surveillance systems","Compare multiple tracker implementations on the same ground truth to identify which performs best","Generate detailed per-frame event logs (false positives, identity switches) to debug tracker failures","Integrate tracker evaluation into a continuous testing pipeline for computer vision models"],"what_it_does":"py-motmetrics is a Python library for evaluating multiple object tracker performance against ground truth annotations. It implements standard MOT metrics (MOTA, MOTP) and global ID measures (IDF1, IDP, IDR) used in the MOTChallenge benchmarks. The library handles the core challenge of multi-object tracking evaluation: finding optimal correspondences between predicted and ground truth objects across video frames, accounting for false positives, misses, and identity switches.\n\nThe package uses pandas DataFrames to track per-frame events (matches, false alarms, track switches) and supports pluggable assignment solvers (scipy, ortools, munkres). Results are compatible with MOTChallenge output formats, making it useful for comparing tracker implementations or validating new detection/tracking pipelines against published benchmarks.","worth_installing":"Yes. The package fills a specific, well-defined role in multi-object tracking evaluation with low install friction, active maintenance, no security vulnerabilities, and permissive licensing. It is the standard choice for MOTChallenge-compatible benchmarking. Install it if you need to evaluate or compare object trackers; skip it if your use case does not involve multi-object tracking metrics."},"id":"motmetrics","links":{"html":"https://skillfed.io/packages/motmetrics","md":"https://skillfed.io/packages/motmetrics.md","pypi":"https://pypi.org/project/motmetrics/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2022-12-26","license_spdx":null,"license_treatment":"permissive","name":"motmetrics","python_support":"unspecified","summary":"Metrics for multiple object tracker benchmarking."},"popularity":{"monthly_downloads":127585,"position":11737,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.4.0"}