Abstract: Aiming at the disadvantage that the regularization filter defines the regularization term in advance but cannot suppress the learning of non-target region in real time,a new method of multi-feature-aware spatiotemporal adaptive correlation filtering target tracking is proposed.Firstly,the spatial local response variation is introduced into the objective function to realize the spatial regularization,so that the filter can focus on the trustworthy part of the learning target,and then the response model is obtained.Secondly,the update rate of the filter is determined according to the change of the global response.Finally,the non-convolution feature level fusion is realized by cascading color histograms(CN) and dimensionally reduced gradient histograms(fHOG).The conv1 and conv5 layers of imagenet-vgg-2048 are used to extract the spatial contour and semantic information of the target.The ReLU function is used to fit and train the data to improve the speed while retaining the main information.Results:In this paper,we compared 8 algorithms of the same type,and used the defined baseline algorithm STRCF(2018) in the objective function,and KCF(2014),which introduces gauss kernels to increase computational speed and sample circularly using a cyclic matrix,and MOSSE_CA(2021),which links context and scale filters,and DCF_CA(2017),which increases the number of samples but reduces the search area Staple(2016) with temporal regularization;region constraint to reduce anomalous ARCF(2019);correlation filter HSTDCF_CA(2021) with hierarchical spatiotemporal map regularization;and target segmentation into four blocks,the SAME_CA(2020) of the scale factor is calculated by using the kernel correlation filter to find the maximum response position of each block.Compared with the accuracy(0.737) and success rate(0.760) of STRCF algorithm,the accuracy rate(0.747) and success rate(0.789) of DTB70 were increased by 1% and 2.9% respectively.Conclusion:The image information learned after multi-layer feature fusion is updated to obtain the overall contour,so as to adaptively track the target.A large number of experiments show that the algorithm basically meets the real-time requirements in complex background,object occlusion,fast motion and other scenarios.

Key words: Target tracking, Correlation filter, Spatio-Temporal adaptation, Local response and global response, Convolutional neural network, Feature fusion