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Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | Please contact Prof. Xiaoming Fu [fu@cs.uni-goettingen.de](B/M/P) Dr. Tingting Yuan [tingting.yuan@cs.uni-goettingen.de] (B/M/P) | ||
=== * | === * [Occupied] Video analytics with deep reinforcement learning === | ||
The proliferation of video analytics is facilitated by the advances of deep learning and the low prices of high-resolution network-connected cameras. However, the accuracy improvement from deep learning is at the high computational cost. Although the state-of-the-art smart cameras can support deep learning method, the deployed surveillance and traffic camera paint a much bleaker resource picture. For example, DNNCam that ships with a high-end embedded NVIDIA TX2 GPU costs more than $2000 while the price of deployed traffic cameras today ranges $40-$200; these cameras typically loaded with a single-core CPU only provide very scarce compute resource. Because of this huge gap, typical video analytics applications, e.g., traffic cameras stream live video to remote server for further analysis. | The proliferation of video analytics is facilitated by the advances of deep learning and the low prices of high-resolution network-connected cameras. However, the accuracy improvement from deep learning is at the high computational cost. Although the state-of-the-art smart cameras can support deep learning method, the deployed surveillance and traffic camera paint a much bleaker resource picture. For example, DNNCam that ships with a high-end embedded NVIDIA TX2 GPU costs more than $2000 while the price of deployed traffic cameras today ranges $40-$200; these cameras typically loaded with a single-core CPU only provide very scarce compute resource. Because of this huge gap, typical video analytics applications, e.g., traffic cameras stream live video to remote server for further analysis. |
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