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Received:March 16, 2016
Received:March 16, 2016
中文摘要: 针对存在更复杂运动模式的无序运动人群密集场景，提出了一种基于多层自适应聚类模型的分群检测算法.以基于高斯混合模型的背景去除算法和自适应初始化聚类算法为核心，通过建立多层自适应聚类模型实现密集人群的分群检测.实验数据库选用了大量真实室内外密集人群运动场景视频，并通过大量对比实验验证了算法的有效性、可靠性和优越性.
Abstract:A hierarchical adaptive clustering based group detection algorithm is proposed for the crowded scenes involving multiple complex motion modalities.Gaussian Mixture Models based background subtraction algorithm and the adaptive initialization clustering algorithm are the key to the algorithm.Group detection is implemented by merging spatiotemporal features of salient points into different layers of the model.Our dataset is built by varieties of in-door and out-door real scene videos.The proposed algorithm outperforms many other algorithms in terms of its effectiveness,reliability and superiority by experimental comparisons.
文章编号：20171020 中图分类号： 文献标志码：
基金项目:国家自然科学基金（61302151，61401268）；上海市自然科学基金（13ZR1455100，15ZR1418400）.
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