Self-adaptive feature fusion-based multi-scale correlation filtering visual tracking method

Abstract

The invention discloses a self-adaptive feature fusion-based multi-scale correlation filtering visual tracking method. The method comprises the following steps: firstly, the correlation filtering is carried out on a target HOG feature and a target color feature respectively by using a context-aware correlation filtering framework; the response values under the two features are normalized; weightsare distributed according to the proportion of the response values and then are subjected to linear weighted fusion, so that a final response graph after fusion is obtained; the final response graph is compared with a pre-defined response threshold value to judge whether the filtering model is updated or not; finally, a scale correlation filter is introduced in the tracking process, so that the scale adaptability of the algorithm is improved. The method can be used for tracking various features. The performance advantages of the features are brought into play, and a model self-adaptive updating method is designed. In addition, a precise scale estimation mechanism is further introduced. According to the invention, the updating quality and the tracking precision of the model can be effectively improved, and the model can be changed in scale. The method is good in robustness under complex scenes such as rapid movement, deformation, shielding and the like.

Description

technical field [0001] The invention relates to the field of visual tracking of computer vision, in particular to a multi-scale correlation filtering visual tracking method of adaptive feature fusion. Background technique [0002] Visual tracking is a basic research problem in the field of computer vision, and it is widely used in video surveillance, unmanned driving, human-computer interaction, military guidance and other fields. Although it has been well developed in the past ten years, and a large number of classic and excellent algorithms have been proposed one after another, it is still a very challenging problem, and there are many interferences from external factors, such as illumination changes, fast motion, occlusion and deformation. Wait. How to achieve more accurate tracking and better adapt to the challenges of various complex scenes is an important topic in the field of visual tracking research. [0003] On the one hand, the current single-feature visual track...

AI Technical Summary

Problems solved by technology

[0003] On the one hand, the current single-feature visual tracking algorithm cannot better adapt to the challenges of complex scenes, and is often affected by the scale change, deformation, fast motion and occlusion of the target, which causes the tracking frame to drift, which in turn leads to tracking failure.

On the other hand, when the target encounters complex scene changes during the tracking process, some wrong tracking information will be generated during this period, which will be introduced into the model update process and will be passed to the In the next frame, long-term accumulation will cause the quality of the model to deteriorate, and eventually lead to tracking failure

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