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Multi-Dimension Support Vector Machine Based Crowd Detection and Localisation Framework for Varying Video Sequences

机译：基于多维支持向量机的可变视频序列人群检测和定位框架

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摘要

In this paper, we propose a novel method for anomalous crowd behaviour detection and localization with divergent centers in intelligent video sequence through multiple SVM (support vector machines) based appearance model. In multi-dimension SVM crowd detection, many features are available to track the object robustly with three main features which include 1) identification of an object by gray scale value, 2) histogram of oriented gradients (HOG) and 3) local binary pattern (LBP). We propose two more powerful features namely gray level co-occurrence matrix (GLCM) and Gaber feature for more accurate and authenticate tracking result. To combine and process the corresponding SVMs obtained from each features, a new collaborative strategy is developed on the basis of the confidence distribution of the video samples which are weighted by entropy method. We have adopted subspace evolution strategy for reconstructing the image of the object by constructing an update model. Also, we determine reconstruction error from the samples and again automatically build an update model for the target which is tracked in the video sequences. Considering the movement of the targeted object, occlusion problem is considered and overcome by constructing a collaborative model from that of appearance model and update model. Also if update model is of discriminative model type, binary classification problem is taken into account and overcome by collaborative model. We run the multi-view SVM tracking method in real time with subspace evolution strategy to track and detect the moving objects in the crowded scene accurately. As shown in the result part, our method also overcomes the occlusion problem that occurs frequently while objects under rotation and illumination change due to different environmental conditions.
机译：在本文中，我们提出了一种新的方法，用于通过基于多个SVM（支持向量机）的外观模型对智能视频序列中的发散中心进行异常人群行为检测和定位。在多维SVM人群检测中，许多功能可通过以下三个主要功能强大地跟踪对象：1）通过灰度值识别对象; 2）定向梯度直方图（HOG）和3）本地二进制模式（ LBP）。我们提出了两个更强大的功能，即灰度共生矩阵（GLCM）和Gaber功能，以实现更准确和可靠的跟踪结果。为了组合和处理从每个功能获得的相应SVM，在基于熵方法加权的视频样本的置信度分布的基础上，开发了一种新的协作策略。我们采用子空间演化策略通过构建更新模型来重建对象的图像。同样，我们从样本中确定重建误差，并再次针对视频序列中跟踪的目标自动建立更新模型。考虑到目标对象的运动，通过从外观模型和更新模型构建协作模型，可以解决并解决遮挡问题。同样，如果更新模型属于判别模型类型，则将二进制分类问题考虑在内并通过协作模型来克服。我们使用子空间演化策略实时运行多视图SVM跟踪方法，以准确跟踪和检测拥挤场景中的运动物体。如结果部分所示，我们的方法还克服了由于环境条件不同而在旋转和光照下的对象发生变化时频繁发生的遮挡问题。

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