An FPGA Implementation for Image Interpretation Based on Adaptive Boosting Algorithm in the Real-Time Systems

This paper presents an FPGA architecture for objects classification based on Adaptive Boosting algorithm. The ar-chitecture uses the color and texture features as input attributes to discriminate the objects in a scene. Moreover, the architecture design takes into account the requirements of real-time processing. To this end, it was optimized for reusing the texture feature modules, giving, in this way, a more complete model for each object and becoming easier the object-discrimination process. The reuses technique allows to increase the information of the object model without decrease the performance or drastically increase the area used on the FPGA. The architecture classifies 30 dense images per second of size 640 × 480 pixels. Both, classification architec ture and optimization technique, are described and compared with others architectures founded in the literature. The conclusions and perspectives are given at the end of this document.