Aiming at the existing problems of object tracking in real scenes, such as complex background, illumination changes, fast motion, and object rotation, the paper has proposed an object tracking algorithm via adaptive multi-feature fusion. By extracting the HOG feature of the object and using convolutional neural networks to extract high-level and low-level convolutional features, an adaptive threshold segmentation method has been used to evaluate the effect of each feature, and the weight ratio of feature fusion has been obtained. The response map of each feature has fused according to the weight coefficient, and the new estimated position of the object has been obtained, and the object scale has been calculated by the scale correlation filter, and the object scale has been obtained to complete the object tracking. The experimental results had conducted on the OTB-2013 dataset. The two-layer convolutional feature and the HOG feature are adaptively fused, so that the more discriminative single feature fusion weight is greater, which better expresses the appearance model of the object, and shows strong object tracking accuracy in scenes such as complex background, the disappearance of the object, light change, fast movement, and rotation.
For visual object tracking in the block motion blur deformation background interference and other issues, put forward in combination with characteris-tics of multiple characteristic scale estimate of background perception related filter tracking algorithm, it through in the object area will be the basis of original algorithm and expand, increase extraction Histogram of Oriented Gradient (HOG) and the characteristics of Color Names (CN) to learn more background information filter, improve object localization accuracy. On this basis, the binary matrix is reasonably constructed to improve the effective response of the filter to the object region on the premise of effectively sup-pressing the background, and the size of the object is estimated by using the training scale filter. Experimental and simulation results show that the pro-posed algorithm can solve the problems such as background interference of occlusive motion blur deformation in tracking. In OTB-100 datasets, the ac-curacy and success rate of proposed algorithm are improved by 1.3% and 1.4% respectively. In the background interference sequence of occluding mo-tion blur deformation of OTB-100 datasets, the accuracy of the proposed al-gorithm is 1.9%, 4.0%, 4.3% and 3.4% higher than that of the Backline-Aware Correlation Filters (BACF) algorithm. The FPS can reach 13.7, this result can show that it has high theoretical value and engineering value.
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