Fast deep pyramid DPM object detection with region proposal networks
Object detection is fundamental for realization of self-driving cars and smart traffic management. Various vision based techniques are based on Deformable part models (DPMs) and recently convolutional neural network (CNN) for its anticipated high precision. The DeepPyramid DPM model maps DPM into multiple CNN layers as an assembly of the two concepts with intensive computations. While the recent Region Proposal Network (RPN) allows convolutional layers sharing with further detection steps to accelerate Fast R-CNN. Our framework employs these cost-free RPN proposals and DenseNet with Fast Fourier Transform (FFT) to introduce a faster and more accurate class independent and region based DeepPyramid DPM detector. Our evaluation on four different benchmarks shows that our technique processes PASCAL VOC at 20 fps and VGA images at 14 fps on a GPU (about 12x faster than DeepPyramid DPM with better precision).