基于改进VGG16的自编码器视频异常检测算法

Auto-encoder Video Anomaly Detection Algorithm Based on Improved VGG16

在使用自编码器结构的神经网络处理视频异常检测任务时,U-Net风格的自编码器由于编码器层数深度过浅,导致在面对复杂的数据集时,不能充分抽取更多有用的特征信息。同时,在训练模型时使用MSE(均方误差),仅考虑了预测帧与真实帧之间的像素级相似性,对于复杂场景,像素级相似性可能无法准确判断预测帧与真实帧之间的相似性。针对以上问题,对基于U-Net风格的自编码器进行改进,提出了一种使用改进的VGG16作为编码器的视频异常检测算法,同时在均方误差的基础上添加结构相似性(SSIM)损失函数。改进的VGG16去掉了全连接层,并加入了残差连接防止特征退化,添加SSIM在计算像素级相似性的同时计算图像的亮度、对比度和结构等方面的相似性来优化网络。实验结果表明,改进后的算法,在Ped2数据集上检测效果达到95.91%,在Avenue数据集上检测效果达到84.89%,与改进前的方法相比分别提高了0.80%和0.19%,验证了所提方法的有效性。

When using the auto-encoder structure neural network to process video anomaly detection tasks,the U-Net style auto-encoder cannot fully extract more useful feature information when facing complex data sets due to the shallow depth of the encoder layer.At the same time,when training the model,MSE is used,only considering the pixel level similarity between the predicted frame and the real frame.For complex scenes,pixel level similarity may not accurately determine the similarity between the predicted frame and the real frame.To solve the above problems,the U-Net style auto-encoder is improved,and a video anomaly detection algorithm using the improved VGG16 as the encoder is proposed.At the same time,the structure similarity(SSIM)loss function is added on the basis of MSE.The improved VGG16 removes the fully connected layer and adds residual connections to prevent feature degradation.SSIM is added to optimize the network by calculating pixel level similarity while also calculating image brightness,contrast,and structural similarity.The experimental results show that the improved algorithm achieves a detection performance of 95.91%on the Ped2 dataset and 84.89%on the Avenue dataset,which is 0.80%and 0.19%higher than that of the previous method,respectively,verifying the effectiveness of the proposed method.