C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library.

github.com/liuliu/ccv
CCV homepage screenshot

About CCV

CCV is a C-based computer vision library for image classification, frontal face detection, pedestrian and car detection, text detection, general object tracking, and feature point extraction. It includes a built-in cache for preprocessing steps such as image pyramid generation and color space conversion while keeping function interfaces simple.

It runs on systems with a C compiler, including Mac OSX, Linux, FreeBSD, Windows, iPhone, iPad, Android, and Raspberry Pi. Most of the library works without compilation flags or dependencies, except convolutional network functions, which require a BLAS library.

Key features

  • Frontal face detection
  • Pedestrian and car object detection
  • Text detection in images
  • Long-term general object tracking
  • Feature point extraction
  • Built-in cache for image pyramids and color conversion
Best for: Developers who need a portable C library for vision tasks in server, mobile, and embedded deployments.
Good for
  • Runs on multiple desktop, mobile, and small-device systems with a C compiler
  • Most functions work without compilation flags or added dependencies
  • Caches preprocessing steps while keeping function interfaces simple
Watch out for
  • Convolutional network functions require a BLAS library

CCV at a glance

Type
Open-source project
Pricing
Not confirmed. Check the site
Platforms
Android, iOS, Linux, Windows
Open source
Not stated
Language
English
Last checked
Oct 6, 2026 · Online when checked
Listed
Oct 5, 2026

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CCV FAQ

What platforms can CCV run on?
Mac OSX, Linux, FreeBSD, Windows, iPhone, iPad, Android, and Raspberry Pi, and generally anything with a proper C compiler.
What dependencies does CCV require?
Most of CCV works with no compilation flags or dependencies, except convolutional networks, which require a BLAS library.
Under what license is CCV distributed?
Source code is distributed under BSD 3-clause License, and data models and documentations are distributed under Creative Commons Attribution 4.0 International License.
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