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 has introduced a new real-time object detection method that combines the efficiency of cascade classifiers with the accuracy of deep neural networks.
- This approach is particularly important for applications like advanced driver assistance systems and surveillance systems, where high accuracy and real-time speed are crucial.
- The new method can process images in 67 milliseconds on a GPU, achieving a speed of 15 frames per second, and was trained using the 'cuda-convnet2' code on an NVIDIA Tesla K20 GPU.
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