Published October 26, 2017 | Version v1
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A Flexible Parallel Hardware Architecture for AdaBoost-based Real-Time Object Detection

  • 1. KIOS Center of Excellence, University of Cyprus


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.


© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, in-cluding reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to serv-ers or lists, or reuse of any copyrighted component of this work in other works. C. Kyrkou and T. Theocharides, "A Flexible Parallel Hardware Architecture for AdaBoost-Based Real-Time Object Detection," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 19, no. 6, pp. 1034-1047, June 2011. doi: 10.1109/TVLSI.2010.2048224


A Flexible Parallel Hardware Architecture for AdaBoost-based Real-Time Object Detection.pdf