CCV is a C-based computer vision library designed for portable, embeddable deployment across diverse platforms from servers to mobile devices and embedded systems.
The library addresses the gap between high-performance but aging algorithm implementations and newer research-grade code that lacks production robustness. CCV takes an application-driven approach, implementing a curated set of state-of-the-art algorithms including image classification, frontal face detection, object detection for pedestrians and cars, text detection, long-term object tracking, and feature point extraction. A distinctive feature is its built-in cache mechanism for image preprocessing operations like pyramid generation and color space conversion, which eliminates redundant computations while maintaining a clean API interface.
CCV suits projects requiring computer vision capabilities on resource-constrained or embedded platforms where dependency management is critical. Its minimalist design philosophy means most functionality works without compilation flags or external dependencies, with the primary exception being convolutional networks which require a BLAS library. The library runs on macOS, Linux, FreeBSD, Windows, iOS, Android, and Raspberry Pi—essentially any platform with a C compiler. This makes it particularly valuable for server-side deployments and mobile applications where simplicity of integration matters as much as algorithmic performance.
The project maintains active continuous integration across multiple platforms including Linux with CUDA support, macOS on ARM64, and Raspberry Pi, with test coverage tracking and static analysis runs. Development follows an application-driven philosophy focused on implementing algorithms that solve real problems rather than comprehensive coverage of the research landscape. The codebase prioritizes clean implementation over feature breadth, reflecting a sustained commitment to the original minimalist vision established over a decade ago.
