An ARM Based Hardware Architecture for Image Processing

Image processing and Object detection applications are often associated with real-time performance constraints that stem from the embedded environment that they are often deployed in. The aim of the project is to design and develop a hardware implementation using advanced features of ARM 9 architecture. The proposed embedded system uses ARM 32 bit RISC CPU. It has features of image/object detection and video processing by using various features and classification algorithms for object detection. Image processing is a form of signal processing for which the input is an image, such as a photograph or video frame; the output of image processing may be either an image or a set of characteristics or parameters related to the image. The used Algorithms and Hardware architecture overcomes the performance in terms of sensors and hardware cost is also low. So, our designed embedded system that detects partially visible human being faces just as they enter the camera view, with low false alarm rate and high speed. This system capture image by means of camera connected to ARM9 microcontroller through USB and the image is detected and processed using image processing technique like Haar object detection and Viola-Jones Detection Algorithm. The captured image undergoes spatio temporal reference samples in terms of both back ground and fore ground estimation, evaluation and spatial Gaussian kernel to provide high quality image of object detection that detected image is continuously displayed on display unit and data is stored in pen drive connected to it. The detected image is compared with the stored face.xml in ARM, if a face is detected in image, the count is of detected face is increased and the image is stored in the USB storage device for further analysis. On connected the USB with PC with internet, an automated email system will take the images, attach it with an email and the email will be sent automatically to a given email