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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications
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An efficient algorithm for tracking and counting pedestrians based on feature points in video surveillance applications

机译：基于视频监控应用中的特征点的跟踪和计数行人的高效算法

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Object tracking is an efficient technique adopted in video surveillance applications for monitoring a particular object in a zone. This paper presents a novel efficient tracking and counting approach for Pedestrians in video sequences. For object detection, a Gaussian Mixture Model (GMM) is used to obtain binary masks. In the Speeded Up Robust Feature feature recognition, only the features of the object are retained. This significantly improves the precision of the Speeded Up Robust Feature method. For clustering the features, an enhanced grouping algorithm Density based Spatial Clustering of Application with Noise is proposed in which the motion features are grouped and the remaining features are excluded. The features are tracked based on optical flow method. For counting the number of objects, the Pedestrian eigen vectors are created based on the Speeded Up Robust Features and the eigen vectors are trained with a SVM (support vector regression machine). The proposed work combines the object detection, feature extraction, and objects counting. The experimental results validate that the proposed pedestrian tracking and counting method is efficient than the existing approaches.
机译：对象跟踪是视频监控应用中采用的有效技术，用于监视区域中的特定对象。本文介绍了视频序列中行人的新型高效跟踪和计数方法。对于对象检测，使用高斯混合模型（GMM）来获得二进制掩模。在加速稳健的特征功能识别中，仅保留对象的功能。这显着提高了加速鲁棒特征方法的精度。为了聚类特征，提出了一种增强的分组算法基于应用的应用程序的缺点，其中分组了运动特征，并且排除了剩余的特征。基于光学流量方法跟踪该特征。为了计数物体的数量，基于加速的鲁棒特征来创建行人EIGEN矢量，并且初始向量用SVM培训（支持向量回归机器）。所提出的工作结合了对象检测，特征提取和对象计数。实验结果验证了所提出的行人跟踪和计数方法比现有方法有效。

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