摘要
基于U-net风格的无监督视频异常检测模型有着较好的检测效果,但由于普通卷积运算使用固有的局部特性,使U-Net风格的编码器无法有效地提取全局上下文信息,并且使用简单的跳跃连接无法获得有效的特征信息,使用的L2损失函数是仅考虑了像素级别的差异而无法捕捉图像的结构特征。对此提出了结合混合卷积和多尺度注意力的视频异常检测算法,并加入结构相似性损失函数(SSIM)优化模型。具体来说,在编码器最后一层添加混合卷积模块,混合空间和位置的特征来提取全局上下文信息。在编码器和解码器之间的跳跃连接中添加多尺度注意力模块,使模型能提取更有价值的特征,实现有效的跳跃连接。使用参数约束结构相似性损失函数与L2损失函数的权重,从而更准确地优化模型。实验结果表明,所提算法在UCSD-Ped2和CUHK Avenue公开数据集上的AUC指标达到96.7%和86.1%,与改进前的模型相比提高了1.6%和1.4%,证明了所提模型的有效性。
Unsupervised video anomaly detection model based on U-net style has good detection results,but due to the inherent local nature of ordinary convolutional operations use,the U-Net style encoder can not effectively extract the global contextual information,the use of simple jump connections can not obtain effective feature information,and the use of the L2 loss function only considers the pixel level differences and can not capture the image’s structural features.In this regard,a video anomaly detection algorithm combining hybrid convolution and multi-scale attention is proposed,and a structural similarity loss function(SSIM)optimisation model is added.Specifically,a hybrid convolution module is added to the last layer of the encoder,which mixes spatial and positional features to extract global contextual information.A multiscale attention module is added to the hopping connection between the encoder and the decoder,which enables the model to extract more valuable features for effective hopping connection.The weights of the structural similarity loss function and the L2 loss function are constrained using parameters to optimise the model more accurately.Experimental results show that the proposed algorithm achieves AUC metrics of 96.7%and 86.1%on the UCSD-Ped2 and CUHK Avenue public datasets,which is an improvement of 1.6%and 1.4%compared with the pre-improvement model,proving the effectiveness of the proposed model.
作者
杨大为
刘志权
王红霞
YANG Dawei;LIU Zhiquan;WANG Hongxia(School of Information Science and Engineering,Shenyang Ligong University,Shenyang 110159,China)
出处
《液晶与显示》
CAS
CSCD
北大核心
2024年第8期1128-1137,共10页
Chinese Journal of Liquid Crystals and Displays
基金
辽宁省自然科学基金面上项目(No.2022-MS-276)
国家自然科学基金青年项目(No.62102272)。
