基于考场监控视频的智能监考方法研究

发布时间：2019-03-16 21:04

【摘要】：随着标准化考场建设的推进,传统视频监控技术监控效率低、海量视频存储压力大的缺点逐渐突显出来。智能化监考系统是智能行为分析技术的一个应用方面,可以杜绝监考不严,提高监控效率,缓解视频存储压力。因此,基于考场视频监控的智能监考方法的研究具有非常重要的实际意义,不仅减小了人力财力物力的投入,也提高了考试公平性。本文在考场环境下提出了一套智能监考方法,适用于考生出勤情况记录和对考生异常行为进行智能检测。本文工作将从以下四个方面展开:(1)通过对考生坐姿特点的观察,提出基于考生头肩部位的目标检测方法。结合方向梯度直方图特征和等价局部二值模式直方图特征,构建融合特征。采用支持向量机分别结合单一特征和融合特征训练分类器,在考生实验数据集上进行目标检测实验并分析检测性能。提出采用基于分类器级联的考生检测框架,以满足检测率和检测速度的双重要求。(2)考虑到考场环境的特殊性,提出了基于YCbCr颜色空间的肤色、发色检测和随机抽样一致性的误差处理方法来修正考生检测结果,以达到考生人数统计和出勤情况记录的目的。(3)提出了基于稀疏重建的考生异常行为检测方法,采用时空梯度特征描述考生行为的外观特征,通过提取运动关注区域和主成分分析的方式简化原始样本数据以减少计算量。对考生常规行为样本数据进行稀疏组合学习并建立模型,通过该模型对每个测试样本计算相应的重建误差,以此完成考生异常行为检测。在本文实验数据集上,该方法可以取得较高的检测性能,并且可以达到实时检测的速度。(4)针对基于稀疏重建的考生异常行为检测方法性能上的不足,本文增加了基于运动历史图像的运动连通域检测方法,形成基于多信息融合的双通道检测框架。双通道下,考生异常行为的检测性能得到有效提升。将本文方法与其它常见的考生可疑行为检测方法进行对比和分析,通过实验证明了本文提出的方法具有更好的普适性。
[Abstract]:With the development of standardized examination room, the shortcomings of traditional video surveillance technology, such as low efficiency and high pressure of mass video storage, are becoming more and more obvious. Intelligent invigilation system is an application aspect of intelligent behavior analysis technology. It can eliminate invigilation, improve the efficiency of monitoring and relieve the pressure of video storage. Therefore, the research of intelligent invigilation method based on video surveillance has very important practical significance, which not only reduces the investment of manpower, finance and material resources, but also improves the fairness of examination. In this paper, an intelligent invigilation method is proposed, which can be used to record the attendance of examinees and to detect the abnormal behavior of examinees. The work of this paper will be carried out from the following four aspects: (1) by observing the characteristics of sitting posture of examinees, a target detection method based on the head and shoulder of examinees is proposed. Combining the directional gradient histogram feature and the equivalent local binary pattern histogram feature, the fusion feature is constructed. Using support vector machine (SVM) to train classifier with single feature and fusion feature respectively, the target detection experiment was carried out on the data set of examinee experiment and the detection performance was analyzed. The framework of examinee detection based on classifier concatenation is proposed to meet the double requirements of detection rate and speed. (2) considering the particularity of examination environment, the color of skin based on YCbCr color space is proposed. The error processing method of color detection and random sampling consistency is used to correct the test results in order to achieve the purpose of examinee number statistics and attendance record. (3) A sparse reconstruction-based method for examinee abnormal behavior detection is proposed. The spatio-temporal gradient features are used to describe the appearance features of examinees' behavior. The original sample data is simplified by extracting the region of motion concern and principal component analysis to reduce the computational complexity. The sample data of routine behavior of examinee is studied by sparse combinatorial learning and the model is built. The corresponding reconstruction error is calculated for each test sample by this model, and the abnormal behavior of examinee is detected by this model. In this paper, the experimental data set, this method can achieve a higher detection performance, and can achieve real-time detection speed. (4) aiming at the sparse reconstruction-based examinee abnormal behavior detection method performance deficiencies, In this paper, a motion connectivity region detection method based on motion history image is added to form a dual-channel detection framework based on multi-information fusion. Under the dual channel, the performance of examinee abnormal behavior detection is improved effectively. The proposed method is compared and analyzed with other common methods of examinee suspicious behavior detection. The experiments show that the proposed method has better universality.
【学位授予单位】：中国科学技术大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP391.41;TN948.6

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