# Sharpiless/Yolov5-deepsort-inference

Yolov5 deepsort inference，使用YOLOv5+Deepsort实现车辆行人追踪和计数，代码封装成一个Detector类，更容易嵌入到自己的项目中

Repository: https://github.com/Sharpiless/Yolov5-deepsort-inference
Canonical: https://ross.abutalabs.com/products/yolov5-deepsort-inference
Language: Python
License: GPL-3.0
License Family: copyleft
Topics: yolov5-deepsort-inference, yolov5, deepsort, object-detection, mot, tracking
Last push: 2026-03-26T10:15:05+00:00

## Health v2 (maintenance only)
Score: 66/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 74, release rhythm 35, longevity 100
- inputs: {"age_days": 2072, "days_push": 160, "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 1479, forks 288 (observed 2026-08-28T04:04:50.713028+00:00)

## What it is
A Python library combining YOLOv5 object detection with DeepSort multi-object tracking to detect, track, and count vehicles and pedestrians in video streams. Detection and tracking logic is wrapped in a reusable Detector class for easy integration into custom projects.

## Use cases
- track vehicles and pedestrians in video
- count cars passing through a video stream
- count people in a crowd video
- integrate object tracking into my python project
- run yolov5 detection with deepsort tracking
- multi-object tracking on cctv footage

## When to choose
- you need ready-made vehicle or pedestrian detection, tracking, and counting with YOLOv5
- you want a simple Detector class to embed tracking into an existing pipeline
- you want to swap in your own trained YOLOv5 weights

## When to avoid
- you need the latest YOLO versions or state-of-the-art trackers
- you need production-grade performance tuning or long-term support
- you work outside Python or need non-GPL licensing

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing, video-processing, machine-learning, deep-learning
- domain: computer-vision, artificial-intelligence, deep-learning
- platform: python, cross-platform
- tags: yolov5, deepsort, object-tracking, object-detection, multi-object-tracking, vehicle-counting, pedestrian-tracking, video, gpu

## Member repositories
- Sharpiless/Yolov5-deepsort-inference (main) score 66

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:50.713028+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 2026-08-30T04:34:17.761949+00:00, confidence not recorded.
  - readme: https://github.com/Sharpiless/Yolov5-deepsort-inference (fetched 2026-08-28T04:04:50.713028+00:00, sha e8f515c25d97)
- Data as of 2026-08-30T08:39:29.467469+00:00.