Computer Digital Technology Combined with Dynamic Visual Communication Sensors in Target Tracking with Big Data

The detection algorithm is explored to improve the dynamic visual sensors (DVS) combined with computer digital technology, build a DVS network, and complete the monitoring and tracking of the target. Ultimately, the problem that needs to be solved is the poor quality of traditional communication sensor data transmission, which needs to be improved by DVS. Firstly, the structure and function of the network are described through dynamic visual perception requirements analysis. Secondly, by introducing a target tracking algorithm that combines event flow and grayscale images, two methods are proposed, namely, the event flow noise reduction method based on event density and the optical flow detection feature tracking algorithm. Finally, through experiments, the tracking and detection effect of the optical flow detection algorithm on the target object in the dark environment is verified in the high-speed motion scene and the reflection environment. The results show that the average error of target object detection and tracking is 3.2 pixels in a dark environment. The average error of target tracking in high-speed motion scenes and reflective environments is 4.86 pixels and 2.88 pixels, respectively. This research has practical reference value for the digital and intelligent development of digital video surveillance systems.