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Multi Object Tracker

Multi Object Tracker is a Python library that associates object detections across video frames using several tracking algorithms, with optional OpenCV-based detector modules.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Multi Object Tracker — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an application already has, or will generate, per-frame detections and needs to associate them over time; the included detector modules can provide detections in supported OpenCV workflows.

Who it is for

  • Python developers building computer-vision applications that need to follow detected objects through video.
  • Developers combining OpenCV-based object detectors with multi-object tracking.

Documented uses

  • Assign and maintain object IDs across successive video frames.
  • Combine supported detectors and trackers in a video analytics pipeline.
  • Produce track records containing frame, ID, bounding-box coordinates and confidence for downstream analysis.
Before you choose

Requirements & limits

The repository is public and identifies its license as MIT. Installation instructions require Python 3.10 or later.

  • Tracker input consists of bounding boxes, confidence scores and class IDs for each frame.
  • The example returns records with frame number, object ID, box coordinates and confidence, alongside additional tuple values.
  • Neural-network model weights must be downloaded separately.
  • GPU use with the provided OpenCV DNN detector modules may require a CUDA-enabled OpenCV build.
  • The README notes implementation variations from the published SORT and IoU Tracker papers.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install the Python tracking package

Install Multi Object Tracker from its GitHub repository for use in a Python project.

Before you start

  • Python 3.10 or later.

Steps

  1. Clone https://github.com/adipandas/multi-object-tracker.

  2. Change into the cloned repository directory.

  3. Run the command below to install the package; use the command below for an editable installation.

    Documented command
    pip install .
    Documented command
    pip install -e .

Expected outcome

The package is installed from the repository for use in a Python project.

Guide limitations

  • This covers installation only, not running a tracking workflow.
  • The project also documents pip installation by package name, but this recipe follows the repository-install steps.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
adipandas/multi-object-tracker · Official homepage title · retained in its source language
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