Fast region-based DPM object detection for autonomous vehicles
Real-time object detection is essential for self-driving cars and traffic monitoring. Deformable part model (DPM) is a popular competitive detector for its burden of intensive computations. We present a fast category independent object detector based on integrated modules to speedup DPM steps. An Edge Boxes region proposal technique is integrated with a feature pyramid approximation and neighborhood aware cascade to accelerate DPM with a comparable precision. Moreover, we employ assembly and SSE optimization with multi-threading to perform detection in real-time. Our experiments on different benchmark datasets show that our proposed framework is 59 times faster than original DPM using a single core. Meanwhile, our optimized implementation can process Pascal VOC at 47Hz and a VGA image at 40HZ with about 2% loss in accuracy.