NORWOOD, USA: Responding to increased developer demand for low cost, fast and efficient video technology software tools, Analog Devices Inc. is now offering a free comprehensive video primitives library for use in real time video analytics applications.
The software modules are fully optimized for the Blackfin processor family and include image processing task level primitives to enable faster development cycles for video analytics applications. The free software modules, available in object code or a C source wrapper, provide advanced video design functionality such as video filtering, transforms, color operations and utilities suitable for a wide range of applications, including video surveillance, automotive vision systems and industrial vision.
The Video Analytics Toolbox Library for the Analog Devices Blackfin processor family is an implementation of imaging tools which can be utilized in the creation of applications such as intrusion detection and left/removed object detection deployed within video surveillance equipment. The library also supports foreground objects/blob detection in video captured from a stationary camera. The C-callable application programming interfaces (APIs) in the library are flexible and can be used in numerous video surveillance use cases.
The Blackfin Image Processing Toolbox modules are bit exact with native ADI APIs and provide C reference codes and wrapper codes for OpenCV-like APIs. Demo code for demonstration of primitive implementations on Blackfin include color conversion, convolution, correlation, sobel filtering, image morphology, kalman filtering, image pyramids, image filters, matrix/vector operations, integral image, and Hough transform.
“By offering these fundamental building blocks for image processing design, we’re enabling developers to streamline the design of powerful, feature-rich imaging solutions,” said Michael Long, Analog Devices’ Security Segment Manager. “These Blackfin-optimized development modules allow solution providers and equipment manufacturers to optimize their analytics implementations, thus reducing the required processing bandwidth, power consumption, and overall cost of the resulting integrated solutions while greatly reducing time to deployment.”
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