Smart cameras with continuous sensing capabilities are ubiquitous today. Over the past several years, powerful image processing and Artificial Intelligence (AI) with Machine Learning (ML) capabilities have become available in highly compact chipsets. These recent innovations have touched every aspect of our lives and industries, ranging from compact, low power recording devices such as dash cams or body cams, and smart homes to extremely powerful industrial cameras running bleeding edge AI analytics. With great computing power comes great expectations from such products. eInfochips is proud to offer a well-integrated, ready to deploy software framework to run on these platforms. The Reusable Camera Framework (RCF) provides a proven, well-tested, must-have feature set for connected, single-sensor or multi-sensor camera solutions that would significantly reduce development effort and time-to-market. This framework is a hardware platform-agnostic solution that allows you to configure key parameters of the camera while providing real-time control over capture, render, and post process activities on video streams or images. It enables the reuse of purpose-built core services to kick-start camera IP development, thus allowing OEMs to focus on breakthrough technology innovations.

Key Features of eInfochips RCF

• Simultaneous recording of every audio-video stream, including search/filter and playback.
• Running cutting edge AI video analytics right on the edge.
• Industry standard cybersecurity protections.
• All standard device management and administration features.
• Faster ONVIF compliance.
• Simultaneous capture of live video and audio from multiple camera sensors.
• Capture of multiple streams (with different configurations) from the same camera sensor.
• Live streaming of the captured audio and video over HLS or RTSP with minimal latency.

Top-Level Architecture of the Framework

Although RCF has been optimized to run on various embedded platforms from leading industry organizations, such as Qualcomm, TI, and Ambarella, most of its components are platform-independent, with only a minimal set of components having platform or vendor-specific dependencies. As a result, porting RCF to a new platform is both fast and easy, which significantly reduces the development time and effort. RCF’s modular, micro-services-based architecture is extremely flexible and customizable through a complete and well-defined set of APIs. Additional services/modules may be integrated with RCF to run on camera or on another device over the network. Any new service integrated with RCF has the same level of fast access to camera data as its any other native component.