Target tracking based on adaptive feature fusion.

Abstract

The invention provides an adaptive feature fusion target tracking method. The adaptive feature fusion target tracking method comprises in a first frame image, initializing a target area, and constructing a location filter and a scaling filter; extracting detection samples around a target, respectively calculating an HOG (Histogram of Oriented Gradient) feature and a CN (Color Name) feature, and obtaining a response value through the location filter; calculating the feature weight according to the response value, normalizing the weight coefficient, fusing the feature response value, and selecting the point having the largest response value as the central location of the target; judging whether a shielding phenomenon occurs according to the target response, under the shielding condition, only updating the scaling filter without updating the target location filter, carrying out a circular process, and obtaining the target location of each frame. The adaptive feature fusion target trackingmethod is advantaged in that an adaptive feature fusion method is provided, a model updating strategy based on APCE (Average Peak-to-Correlation Energy) is designed, and the target tracking precisionand the robustness under the shielding condition can be greatly improved.

Description

technical field [0001] The invention belongs to the fields of image processing and computer vision, and in particular relates to a target tracking method for adaptive feature fusion. technical background [0002] In computer vision, object tracking is a hot research field, which can be applied to many human-computer interaction fields such as video surveillance and automatic monitoring. Although researchers have proposed many tracking algorithms in recent years, it is difficult to achieve real-time efficient and stable tracking in the face of problems such as target appearance changes, fast motion, scale transformation, and occlusion. [0003] The traditional tracking method builds a complex appearance model and extracts a large number of learning samples, which generates a huge amount of calculation, and it is difficult to achieve real-time tracking. Correlation filtering methods bypass the construction of complex appearance models and a large number of learning samples, a...

AI Technical Summary

Problems solved by technology

Although researchers have proposed many tracking algorithms in recent years, it is difficult to achieve real-time efficient and stable tracking in the face of problems such as target appearance changes, fast motion, scale transformation, and occlusion.

[0003] The traditional tracking method builds a complex appearance model and extracts a large number of learning samples, which generates a huge amount of calculation, and it is difficult to achieve real-time tracking.

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