【作者】 李红梅；

【导师】 纪越峰；

【作者基本信息】 北京邮电大学 ， 通信与信息系统， 2013， 硕士

【摘要】 智能楼宇中的视频监控已经受到社会越来越多的关注。智能楼宇中的视频监控是利用数字图像处理、机器学习以及计算机视觉等技术,自动且实时的处理和分析视频图像,对视频监控场景中存在的可疑行人目标、潜在危险或违规行为进行预警或报警。本文针对光照变化,遮挡,快速运动等场景下的行人检测和跟踪问题,深入研究了基于分类器的的行人检测和跟踪方法,本文主要研究工作如下：1)针对光照变化,遮挡,快速运动等场景下的行人检测问题,提出了一种针对感兴趣区域,基于梯度方向直方图和纹理特征相融合的行人检测方法。该方法通过行人跟踪片在行人检测过程中引入时间相关性,即在行人检测过程中引入行人跟踪结果以调整行人检测器中的扫描尺度,缩小检测器扫描的层次和范围。使得行人检测器只针对感兴趣区域(存在行人概率高的区域)进行检测,提高了行人识别率。分类器的特征使用改进的梯度方向直方图和纹理相融合特征。从实验结果发现这种融合的特征不仅提高了分类器的性能,使得行人识别效果上有所提高,而且在一定程度上能够解决行人的局部遮挡问题。2)针对行人跟踪“漂移”问题,研究和分析了基于分类器的跟踪算法。本文提出了将离线行人检测和在线行人跟踪相结合的行人跟踪方法。将离线行人检测作为行人的先验知识引入到跟踪器中,它不依赖自学习过程来更新,因此避免自学习造成错误累积而产生“漂移”。该方法不仅解决了在线行人跟踪的初始化问题,也解决了在线分类器的训练数据的选择问题。由于引入了离线行人分类,当行人被遮挡后重新出现在场景或者跟踪器跟丢的情况下,该方法能重新识别行人,继续对其跟踪,因而实现了长时间行人跟踪。更多还原

【Abstract】 More and more people are paying attention to intelligent video surveillance. Intelligent video surveillance uses technologies such as digital image processing, artificial intelligence and computer vision to process and analyze videos automatically in real-time, which can warn or alarm suspicious targets, potential hazards or violations in monitored scenes.This dissertation researched pedestrian detection and tracking algorithm under different conditions, including illumination change, occlusion and fast movement.Main work of this dissertationis listed as follows:1) To solve the problem ofpedestrian detection and tracking under different conditions, including illumination change, occlusion and fast movement, this dissertation proposed a pedestrian detection algorithm fusing histogram of gradient direction and textural features aiming at the interested areas. During pedestrian detection process, detector adjusts the scanning scale of the pedestrian detector by introducing pedestrian tracking result information to reduce the level and scope of the scanning of the detector. The detector only detects in the region of interest (The area where there is a high probability of pedestrian) to improve the pedestrian detection rate.The features merged histogram of gradient merging and textural features. Experimental results show that the improved histograms of gradient merging with local binary pattern scan not only improve the recognition rate, but can alsoand solve the occlusion problem to a certain extent.2)To solve the "drift" problem in pedestrians tracking, this dissertation researched and analyzed pedestrian tracking algorithm based on classifier.We proposed a method to combine offline pedestrian trackingand online pedestrian tracking templates. The Offline pedestrian detection results can introduce priori knowledgeinto the tracker. And it did not depend on the learning process to update so that it can avoid "drift" caused by the error accumulation of learning. This algorithm not only solved the problem of online pedestrian tracking initialization, but also solved the problem of selection of training data. As offline pedestrian classification, when pedestrians were presented in the scenes again after being occluded or the tracker lost targets, this method can re-recognize the pedestrians and keep tracking, so that the long time tracking can be achieved.更多还原