基于卷积Hopfield网络的运动目标检测模型

Moving Target Detection Model Based on Convolutional Hopfield Network

本文针对视频图像中显著性检测存储量大、计算复杂等特点,提出了一种结合卷积的Hopfield神经网络的深度学习模型。利用Hopfield网络在序列图像处理上的优势,处理视频信息中的能更好地结合上下文关系和时序信息提取运动目标的特征。将Hopfield的循环反馈与卷积结合起来,以在空间上更好地提取运动目标。将传统的Hopfield网络的全连接转换为局部连接的Hopfield网络。用局部连接的HNN作为门控RNN的主要部分代替区域框,并与卷积神经网络结合起来进行显著性检测,然后结合到darknet框架下进行视频运动目标检测。在VIVD数据集下验证显示,针对视频中的运动目标,在无须提前训练和标记的情况下能获得较好的检测结果。

A deep learning model of Hopfield neural network combined with convolution was proposed for the large storage capacity and complex calculation of significance detection in video images. With the advantage of Hopfield network in processing sequential images,video information can be better combined with context and temporal information to extract features of moving targets. The circular feedback of Hopfield is combined with convolution to better extract moving targets in space. Meanwhile, the traditional Hopfield network full connection is transformed into a locally connected Hopfield network in this paper. The locally connected HNN was used as the main part of the gated RNN to rep lace the region frame and was combined with the convolutional n eural network for significance detection,and then combined with darknet for video moving target detection. In the case of VIVD data set, the verification shows that for the moving targets in video,better detection results can be obtained without the need of training and marking in advance.