How to implement automotive smart rear-view cameras

1. Acquire a comprehensive ‘golden’ image validation database and automate the test process. This will enable testing the algorithm during all phases of development and determine if algorithm changes have impacted performance.
2. Be mindful of the embedded platform architecture, its properties and limitations and develop a set of guidelines both platform and algorithm developers must adhere to in an effort to reduce PC algorithm re-writes. 
3. Develop a vision-centric software framework. Algorithm development does not consider data movement and a “software framework” can help manage the complexities of vision processing data movement. The framework can ensure data is always available for processing, reducing pipeline stalls and cache misses.
4. Develop specialized vision libraries to simplify and speed porting onto the target embedded platform. Library functions of high level complex processing “primitives” which are optimized for the specific scalar and vector processor architecture can be reused in follow-on embedded vision application development.
5. Develop a tool for camera calibration and look-up-table (LUT) coefficients instead of generating these manually. The task of generating LUTs for different sensor/lens combinations is tedious and time consuming. We developed a PC-based tool to automatically generate LUTs for creating custom views specific to the camera lens selected.
6. Structure the code to accommodate customizing specific components of the final solution. For example, consider offering the application code in the form of a toolkit whereby elements can be modified and a new build can be generated by simply linking in the lower level algorithm binary executable. 
7. Take all necessary design precautions to ensure a clean signal from the image sensor is acquired by the image processing device.  A noisy signal from the sensor will mean sub-optimal performance.