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社会治安视频监控在公安办案为锁定目标、提供线索、固定证据、辅助审讯等方面提供了有力的技术支撑,已经成为公安机关战斗力的新的增长点。公安办案所采集的视频监控图像多发生在复杂环境(昏暗,烟,雾,阴雨,电磁干扰等)下,为能够从中提取更加有效的信息和数据,本文提出一种基于LVQ神经网络的模式识别方法,来改进视频监控图像效果,通过matlab仿真在理论上验证方法的可行性。
Social security video surveillance has provided strong technical support for the target, clues, fixed evidence, auxiliary interrogation and other aspects in handling public security cases and has become a new growth point for the combat effectiveness of public security organs. The video surveillance images collected by the public security department mostly take place under complex environment (darkness, smoke, fog, overcast and rain, electromagnetic interference, etc.). In order to extract more effective information and data from it, this paper presents a pattern recognition based on LVQ neural network Method, to improve the video surveillance image effects, and verify the feasibility of the method in theory by matlab simulation.