A Feature Fusion Based Object Tracking Algorithm

At present, most of the networks used to extract features in tracking algorithms are pre-trained on the ImageNet classification data set, such as VGG-16, AlexNet, etc. Most of the features used are feature maps output by the highest convolutional layer, so as to make full use of the powerful expressive power of convolutional features. But it will bring certain problems. The spatial resolution ability of convolutional neural network is not strong. If the target moves fast, exceeds the sampling area of the picture, or the target is occluded by background objects, the target tracking algorithm will fail. In order to further improve the spatial resolution ability of the convolutional neural network and solve the problem of tracking failure that may be caused by local cutting, a single target tracking algorithm based on feature fusion is proposed here. The feature map of the last layer is up-sampled and merged with the feature map of the previous layer to improve the spatial resolution ability. At the same time, the information of different feature models is used to improve the accuracy of the tracking algorithm. In addition to the local detection model, this article also proposes a classification detection model. When the local detection model cannot perform effective detection, multiple detection models are used instead to detect within the entire image to find the location where the target may appear. If the location where the target appears is found, the local detection model continues to be used at that location.