Real-time video surveillance has become a crucial technology for smart
cities, made possible through the large-scale deployment of mobile and fixed
video cameras. In this paper, we propose situation-aware streaming, for
real-time identification of important events from live-feeds at the source
rather than a cloud based analysis. For this, we first identify the frames
containing a specific situation and assign them a high scale-of-importance
(SI). The identification is made at the source using a tiny neural network
(having a small number of hidden layers), which incurs a small computational
resource, albeit at the cost of accuracy. The frames with a high SI value are
then streamed with a certain required Signal-to-Noise-Ratio (SNR) to retain the
frame quality, while the remaining ones are transmitted with a small SNR. The
received frames are then analyzed using a deep neural network (with many hidden
layers) to extract the situation accurately. We show that the proposed scheme
is able to reduce the required power consumption of the transmitter by 38.5%
for 2160p (UHD) video, while achieving a classification accuracy of 97.5%, for
the given situation.

本研究提出一种基于情景感知的实时流式传输方案，以实时识别重要事件和降低发射功率。该方案主要利用少量隐藏层的小型神经网络在源端对特定情景下的帧进行高重要性分配，并将高重要性的帧与一定信噪比的低重要性帧同时流式传输，在接收端进行深度神经网络分析提取准确的情景并节约约 38.5% 的能源。