# yehengchen/Object-Detection-and-Tracking

Multi-Object Tracking via DeepSORT

Repository: https://github.com/yehengchen/Object-Detection-and-Tracking
Canonical: https://ross.abutalabs.com/products/object-detection-and-tracking
Language: Python
License: MIT
License Family: permissive
Last push: 2023-03-24T22:21:37+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 2694, "days_push": 1258, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1961, forks 751 (observed 2026-08-28T04:05:59.433282+00:00)

## What it is
A collection of Python implementations combining YOLO-based object detection with SORT and DeepSORT multi-object tracking. It includes examples for pedestrian and vehicle counting, social distancing, and ROS-integrated robot grasp detection.

## Use cases
- track multiple objects in video with deepsort
- count pedestrians in a video stream
- run yolo object detection on custom images
- detect objects with ros for robot grasping
- measure social distance between people in video

## When to choose
- you need a working YOLO + DeepSORT tracking pipeline with reference code
- you want ROS integration for object detection on a robot
- you need examples for pedestrian or vehicle counting

## When to avoid
- you need a production-grade, actively maintained tracking library
- you work outside Linux/ROS or need Windows/macOS support
- you need state-of-the-art trackers beyond SORT/DeepSORT

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, machine-learning, deep-learning
- domain: computer-vision, machine-learning, robotics
- platform: python
- tags: object-detection, object-tracking, deepsort, yolo, sort, pedestrian-counting, ros, linux

## Member repositories
- yehengchen/Object-Detection-and-Tracking (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:59.433282+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:05:40.051626+00:00, confidence not recorded.
  - readme: https://github.com/yehengchen/Object-Detection-and-Tracking (fetched 2026-08-28T04:05:59.433282+00:00, sha f2f8097e5d43)
- Data as of 2026-08-30T08:39:29.467469+00:00.