Purpose

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

Design/methodology/approach

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

Findings

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

Originality/value

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

中文翻译：

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用于高密度人群视频中行为识别的最佳支持向量机和混合跟踪模型

目的

对象检测模型由于在许多应用（例如监视，视频监视等）中的辅助应用而获得了广泛的普及。通过视频跟踪进行对象检测面临许多挑战，因为实时流获得的大多数视频都受到以下因素的影响：环境因素。

设计/方法/方法

本研究开发了一种使用混合跟踪模型的人群跟踪和人群行为识别系统。拟议的人群跟踪系统的输入是包含数百人的高密度人群视频。第一步是通过视觉识别算法检测人。在此，将位置点的先验知识作为视觉识别算法的输入。视觉识别算法通过最小边界矩形（MBR）中定义的约束来识别人。然后，基于空间跟踪模型的跟踪视频帧中人体运动的路径，并通过提取颜色直方图和纹理特征来进行跟踪。此外，基于NARX神经网络模型应用了时间跟踪模型，该模型可有效地用于检测运动物体的位置。一旦跟踪了人的路径，就可以通过结合支持向量机和优化算法（即MBSO）新开发的最优支持向量机来识别每个人的行为。通过集成现有技术（例如BSA和MBO），开发了提出的MBSO算法。

发现

从高人群密度数据集中的跟踪中利用了用于对象跟踪的数据集。所提出的OSVM分类器以0.95的精度获得了改进的性能。

创意/价值

本文提出了一种混合高密度视频跟踪模型和行为识别模型。所提出的混合跟踪模型通过时间跟踪和空间跟踪来跟踪视频中对象的路径。这些功能根据建议的MBSO算法选择的权重训练建议的OSVM分类器。提出的MBSO算法可以看作是BSO算法的修改版本。