复杂场景下的运动目标识别算法

目标识别是计算机视觉的基本目的,同时也是人工智能领域的重要组成部分之一.随着信息化时代的来临,视频采集工具的普及,海量的视频数据给人工识别带来了巨大挑战.现阶段,在智能交通领域、生产质检领域等简单场景中,视频识别技术已经得到广泛的应用.如何从复杂场景中实现目标的识别和检测则成为了更加重要和困难的问题.针对该问题,本文提出了一种复杂场景下的运动目标识别算法.首先,提出一种改进的光流算法,通过时间序列以及空间像素变化对运动目标区域进行快速标记；其次,对目标区域进行滑动窗口检测,匹配人体各部位模型,并将反馈信息利用树形结构进行人体建模,实现在复杂场景下识别运动目标.通过实验进行评估,该方法能够在保证较高准确率的情况下,相比基于深度学习的检测算法检测速度更快,可以满足实时监测的要求.

Recognition Algorithm of Moving Target in Complicated Scenes

Target recognition is the basic purpose of computer vision, it is also one of the key components in the field of artificial intelligence.With the advent of the information age, the popularity of video capture tools, massive video data to human identification has brought great challenges. At this stage, video recognition technology has been widely used in simple scenes such as intelligent transportation field and production quality inspection field. How to realize the target recognition and detection from complex scenes has become a more important and difficult issue. In response to this problem, this paper presents a moving target recognition algorithm in complex scenes. First, an improved optical flow algorithm is proposed to mark the moving target region quickly by time series and spatial pixel changes; Secondly, the sliding window of the target area is detected to match the model of each part of the human body, and the feedback information is modeled by a tree structure. Through experiments, this method can detect faster than detection algorithm based on depth learning while ensuring high accuracy, and can meet the requirements of real-time monitoring.