Recognition-aware Bitrate Allocation for AI-Enabled Remote Video Surveillance

Abstract

With a growing number of deployed video surveillance cameras, deep learning based video recognition becomes increasingly important for analysing the large amounts of generated video content. For use-cases with computationally intensive recognition applications and with moving cameras or frequently changing camera layouts, video content is often streamed in real-time over wireless networks to cloud environments where video recognition technologies operate. However, it is in general difficult to transmit a large number of video streams and also achieve a high video recognition accuracy with only the limited radio frequency resources of wireless networks, because of video compression artefacts appearing at low video bitrates. To allow transmission of more streams and to increase recognition accuracy, we propose a dynamic prediction-based bitrate allocation method that balances encoder bitrates among cameras in a way that approximately minimizes the total recognition error. For predicting the number of recognition errors at given bitrate and video content, our method uses a neural network operating in a distributed fashion at both edge and cloud locations. In the experiments, we show that our predictions are highly correlated with the actual numbers of recognition errors (Pearson correlation of 0.872). Furthermore, an evaluation of our allocation approach in a construction site surveillance scenario with ten video streams shows that at low average video stream bitrates under 1 Mbps, our method can reduce the false negative rate of an action recognition engine by 23% over the baseline approach (evenly distributed allocation) on average.