ADVANCED HARDWARE ARCHITECTUREFOR ADABOOSTBASEDREALTIMEOBJECT DETECTION
Journal Title: International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) - Year 2012, Vol 1, Issue 6
Abstract
Abstract—In this paper, we present a work of new hardware architecture to a Flexible Parallel Hardware Architecture for AdaBoost-Based Real-Time Object Detection. Here, we anticipate that optimizations in terms of power consumption will significantly improve the architecture. Because of optimization of architecture, we get a new hardware, which consumes less amount of power than previous hardware architecture.Real-time object detection is becoming necessary for a wide number of applications related to computer vision and image processing, security, bioinformatics, and several other areas. Existing software implementations of object detection algorithms are constrained in small-sized images and rely on favorable conditions in the image frame to achieve real-time detection frame rates. Efforts to design hardware architectures have yielded encouraging results, yet are mostly directed towards a single application, targeting specific operating environments. Consequently, there is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios.In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms. Through both field-programmable gate array emulation and large-scale implementation, and register transfer level synthesis and simulation, we illustrate that the architecture can detect objects in large images (up to 1024 768 pixels) with frame rates that can vary between 64–139 fps for various applications and input image frame sizes.
Authors and Affiliations
Polani Devi Vara Prasad, , Janakani Devi Vara Prasad, , Malijeddi Murali,
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