融合时序相关性的课堂异常行为识别

Classroom Abnormal Behavior Recognition Based on Sequential Correlation

针对人体行为最重要的motion特征,提出了基于时间上下文的二级递推异常行为识别方法.不同于传统深度学习的训练方法,本文方法不是直接从图像数据中学习特征,而是把提取的形状信息HOG特征作为训练输入.首先提取基于HOG算法的图像形状特征,采用提取到的特征训练DBN网络.其次利用已经训练好的DBN网络和Softmax分类器识别出人体粗目标区,然后根据粗目标区域的时序上下文信息,计算质心加速度.最后判断加速度的阈值,识别出异常行为的精目标区.本文将粗细目标结合的二级递推方法应用到课堂行为识别中,通过实验结果表明,该方法在运动模糊和目标密集遮挡的场景下都能较好地识别出课堂行为,识别率相比其他方法有较大提升.课堂异常行为数据分析,可在课堂动态管理和学习效果评估等方面发挥辅助作用.

Aiming at the most important motion characteristics of human behavior,a second-level recursive anomaly behavior recognition method based on time context is proposed.Different from traditional deep learning training methods,this method does not directly learn features from image data,but extracts them.The shape information HOG feature is used as the training input.Firstly,the image shape feature based on the HOG algorithm is extracted,and the extracted feature is used to train the DBN network.Secondly,the trained DBN network and the Softmax classifier are used to identify the human body coarse target region,and then according to the coarse The time-series context information of the target area,calculate the centroid acceleration.Finally,the threshold of the acceleration is judged,and the precise target area of the abnormal behavior is identified.This paper applies the two-level recursive method combining the weight and the target to the classroom behavior recognition,and the experimental results show that the The method can better recognize the classroom behavior in the scenes of motion blur and target dense occlusion,and the recognition rate is greatly improved compared with other methods.Classroom abnormal behavior data analysis can play a supporting role in classroom dynamic management and learning effect evaluation.