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.
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.
Real-Time Pedestrian Detection using Cascades of Deep Neural Networks
Aug 06, 2015
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AI-Generated Summary
- Google Research developed a real-time object detection approach that combines cascade classifiers with deep neural networks for pedestrian detection.
- The method achieves real-time performance at 67 milliseconds per image on GPU, equivalent to 15 frames per second.
- Training used the publicly available cuda-convnet2 code on an NVIDIA Tesla K20 GPU.
Next Steps
- Explore the cuda-convnet2 repository to review the training implementation.
- Learn more about NVIDIA Tesla K20 GPU specifications for deep learning workloads.
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