Floating search methods in feature selection
Pattern Recognition Letters
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting as entropy projection
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Detecting Pedestrians Using Patterns of Motion and Appearance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
WaldBoost " Learning for Time Constrained Sequential Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Face detection for automatic exposure control in handheld camera
ICVS '06 Proceedings of the Fourth IEEE International Conference on Computer Vision Systems
A Parallel Architecture for Hardware Face Detection
ISVLSI '06 Proceedings of the IEEE Computer Society Annual Symposium on Emerging VLSI Technologies and Architectures
An FPGA-based people detection system
EURASIP Journal on Applied Signal Processing
Communication-aware face detection using noc architecture
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Adaboost with totally corrective updates for fast face detection
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
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Object detection forms the first step of a larger setup for a wide variety of computer vision applications. The focus of this paper is the implementation of a real-time embedded object detection system while relying on high-level description language such as SystemC. Boosting-based object detection algorithms are considered as the fastest accurate object detection algorithms today. However, the implementation of a real time solution for such algorithms is still a challenge. A new parallel implementation, which exploits the parallelism and the pipelining in these algorithms, is proposed. We show that using a SystemC description model paired with a mainstream automatic synthesis tool can lead to an efficient embedded implementation. We also display some of the tradeoffs and considerations, for this implementation to be effective. This implementation proves capable of achieving 42 fps for 320 × 240 images as well as bringing regularity in time consuming.