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.

Read the research paper >>

About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter