GPUs are being used in neural network training and for near-real time execution of complex machine learning algorithms in natural language processing, image recognition, and rapid video analysis.
Pedestrian detection is one of those areas where, when powered by truly accurate and real-time capabilities, could mean an entirely new wave of potential services around surveillance, traffic systems, driverless cars, and beyond.
The complexity involved with real-time pedestrian detection is staggering, especially considering the range of settings, movements, other objects in a particular scene can rapidly change, interact and not just move, but in what manner pedestrians move. These are not new problems, however. Computer vision has long been a topic of research interest, and was certainly lent new possibilities with the parallel rise of GPU computing and more evolved deep neural networks.
The Google Research team expects to keep increasing the depth of the deep networks cascade by adding more tiny deep networks and exploring the efficiency-accuracy trade-offs. We further plan to explore using motion information from the images, because clearly motion cues are extremely important for such applications. Still, they say the challenge continues to rest on doing this at sufficiently high computational speeds.
SOURCE – Nicole Hemsoth at The Platform
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