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Cascade Boosting-Based Object Detection from High-Level Description to Hardware Implementation

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 images as well as bringing regularity in time consuming.