Real-Time Pedestrian Detection using Cascades of Deep Neural Networks

Google Research presents a new real-time approach to object detection that exploits the efficiency of cascade classifiers with the accuracy of deep neural networks. Pedestrian detectors is very important as it relates to a variety of applications including advanced driver assistance systems, or surveillance systems. The need for very high-accurate and real-time speed is crucial that can be relied on and are fast enough to run on systems with limited compute power.

Real-Time Pedestrian Detection With Deep Cascades

The research team combined a fast cascade with a cascade of deep neural networks which is both very fast, running in real-time at 67 milliseconds on GPU per image or 15 frames per second. Their approach was trained using the publicly available ‘cuda-convnet2’ code  running on an NVIDIA Tesla K20 GPU.

About Brad Nemire

Brad Nemire leads the Developer Communications team at NVIDIA focused on evangelizing amazing GPU-accelerated applications. Prior to NVIDIA, he worked at Arm on the Developer Relations team. Brad graduated from San Diego State University.