Fast Video Frame Correlation Analysis for Vehicular Networks by Using CVS-CNN

In vehicular communication systems, due to limited computation power of vehicles, low-cost sampling technologies, such as compressed video sensing (CVS), have been proposed. However, after one-time coarse compressive sampling, it is difficult to obtain accurate temporal correlation between video frames. To address this issue, this paper proposes a correlation analysis model in the measurement domain by combining CVS and convolutional neural network (CNN), which is termed as CVS-CNN. Specifically, to analyze the temporal correlation of video frames in the measurement domain, we use CNN as a substitute for the pseudo-inverse transform of the measurement matrix and establish the correlation between the measurements of the block to be estimated and those of the neighboring non-overlapping blocks. The network parameters are trained to minimize the loss between the predicted and true measurements, and are assigned to the non-overlapping image blocks. The various experimental results demonstrate that the proposed CVS-CNN method significantly outperforms similar methods of analyzing the video frame correlation in accuracy, process speed, and robustness. This result indicates that the proposed method can be used in many potential applications, such as self-driving systems and in-car warning systems.