Boosted multi-class object detection with parallel hardware implementation for real-time applications

Real-time multi-class object detection becomes popular for various applications such as vehicle vision systems, computer vision and image processing. Boosted cascades achieve fast and reliable object detection for one object class, but require parallel usage of multiple cascades for multi-class detection. The multi-class capable cascade splits the root-cascade into sub-cascades iteratively until each sub-cascade contains one class. That requires a huge number of classifiers in the generated hierarchy of interlinked cascades. In this paper, we propose a boosted multi-class object cascade that only splits one class object from the upper-level-cascade when building the sub-cascades. Since only once class object is split so we can reduce the number of classifiers in each stage. From the simulation results, the boosted multi-class object detection can reduce 46% weak classifiers compared to the multi-class capable cascade for the MIT CBCL database. The proposed method achieves high detection rate(95.54%) and low false positive rate(1.94%). We implement our proposed algorithm with a parallel architecture to accelerate the detection operation using TSMC 90nm CMOS technology. The implementation results show that the design achieves an operation frequency of 100MHz of processing images of 30 fps with size 160 × 120.