基于弱线性回归树在线学习的自适应视频目标检测算法研究

项目名称： 基于弱线性回归树在线学习的自适应视频目标检测算法研究

项目编号： No.61302137

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 罗大鹏

作者单位： 中国地质大学（武汉）

项目金额： 25万元

中文摘要： 本项目针对视频图像中的目标检测问题，基于在线学习理论与线性回归树的构造方法，研究具有自主学习能力的目标检测系统。采用弱线性回归树算法构建系统检测模型，利用线性回归树组合特征的能力提高模型检测性能。通过弱线性回归树的系数更新方法实现检测系统的在线学习，加快系统在线学习速度，保证系统持续学习的效果。采用粒子滤波对系统检测到的目标进行跟踪、验证，从中获得在线学习样本，实现检测系统无需人工干预的自适应学习。为了减少验证错误对系统在线学习的影响，引入多实例学习提高系统的鲁棒性。该项目的研究成果将为智能视频监控中的目标检测等实际应用提供一种新方法，并丰富基于在线学习的目标检测、识别理论。

中文关键词： 在线学习；随机蕨分类器；混合分类器；注意机制；物联网

英文摘要： The main research content is designing a intelligent object detection system which can self learning and improve its detection performance based on online learning and linear regress tree theories.The linear regress tree is used to combine multiple features in weak leaning process. And the final strong classifier is composed of several weak linear regress trees. This leads to a better strong classifier, which consists of fewer weak classifiers and features than classical methods.A new online learning method is proposed based on the weak linear regression coefficient update. The resulting system contains enough number of weak classifiers while keeping computation cost low. And the online learning samples are acquired and labeled automatically when they are used to training the detector online.Instead of using another detection algorithm to label the new samples like other online learning frameworks, we ensure the correct labels from the particle filter tracking method. This can greatly reduce the effort by labelers. Furthermore, in order to reduce the impact of validation error, the Multiple Instance Learning（MIL） is used to improve the robustness of the online learning system.The results of the research will provide a new method for the practical application of intelligent video surveillance, and rich the obj

英文关键词： online learning；random fern classifier；hybrid classifiers；attention mechanism；Internet of Things