Sharpiless/Yolov5-Deepsort
最新版本yolov5+deepsort目标检测和追踪，能够显示目标类别，支持5.0版本可训练自己数据集 observed · 2026-08-28
Health v2 · maintenance only
32/100
- Activity 0
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-02. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 1925
- days_rel: n/a
- days_push: 1427
- n_releases_24m: 0
Adoption not part of the score
1154 stars · 171 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded
A Python project combining YOLOv5 object detection with DeepSort multi-object tracking, packaged as a Detector class for vehicle and pedestrian tracking and counting. Supports training custom YOLOv5 models.
Use cases
- track vehicles and pedestrians in video
- count objects crossing a frame
- run yolov5 detection with deepsort tracking
- train custom yolov5 model for detection and tracking
- integrate object tracking into my python project
When to choose
- you need YOLOv5 detection plus DeepSort tracking in one pipeline
- you want a simple Detector class to embed in your project
When to avoid
- you need actively maintained code or recent YOLOv5 versions
- you need tracking beyond person/car/truck classes without modification
Facets
library · maturity maintenance
computer-vision image-processing computer-vision python yolov5 deepsort object-tracking
1 source
- readme: https://github.com/Sharpiless/Yolov5-Deepsort · fetched 2026-08-28 · 57df8f69e2ee
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| Sharpiless/Yolov5-Deepsort | main | 32 |
For agents
markdown · JSON · MCP: product_card(name="Sharpiless/Yolov5-Deepsort")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem