基于图形显著性检测的残差网络特征融合跟踪算法

Residual Network Feature Fusion Tracking Algorithm Based on Graph Salience Detection

目标的特征表达是目标跟踪过程的关键,人工特征相对简单,实时性强,但表征能力不足,在处理快速变化和目标遮挡相关问题时,容易产生跟踪漂移。深度神经网络(DNN)在目标检测和识别任务中的强特征表达能力,使DNN逐渐成为特征提取工具。采用更深层的残差神经网络(ResNet)替代VGG-19网络作为特征提取工具,首先将ResNet-50中的特殊附加层结构和卷积层特征进行融合,得到鲁棒性更强的目标表征特征。然后对特征进行相关滤波操作,根据最大响应值确定目标位置。最后,为扩展算法在局部目标跟踪领域的应用场景,采用基于图形的视觉显著性检测算法提高局部目标的权重值,抑制背景信息,以提升特征层的目标表征能力。

The feature expression of the target is the key to the target tracking process.The artificial features are relatively simple and have strong real-time performance,but the expression ability is insufficient.It is easy to produce tracking drift when dealing with the problems of rapid change and target occlusion.The strong feature expression ability of deep neural network(DNN)in target detection and recognition tasks makes DNN gradually become a feature extraction tool.A deeper residual neural network(ResNet)is used to replace VGG-19 network as a feature extraction tool.First,the special additional layer structure and convolution layer features in ResNet-50 are fused to obtain target representation features with stronger robustness.Then,the feature is filtered and the target position is determined according to the maximum response value.Finally,in order to expand the application scene of the algorithm in the field of local target tracking,a graphic based visual saliency detection algorithm is used to increase the weight value of the local target and suppress background information,so as to improve the target representation ability of the feature layer.