A method for carrying out visual tracking through a spatio-temporal context

Abstract

The invention provides a method for carrying out visual tracking through a spatio-temporal context, comprising the following steps of step 1, initializing parameters; 2, training a context-aware filter to obtain a position model; 3, training the maximum scale response value of a scale correlation filter to obtain a scale model; 4, outputting a response diagram by the classifier; discriminating thepeak sidelobe ratio corresponding to the peak value of the response map generated by the correlation filter; 5, comparing the peak-to-peak sidelobe ratio of the response map, and if the peak-to-peaksidelobe ratio of the response map is greater than the peak-to-peak sidelobe ratio, introducing an on-line random fern classifier for re-detection; if the peak value of the response map is less than the peak sidelobe ratio, updating the position model of the step 2 and the scale model of the step 3; if the peak value of the response map is equal to the peak sidelobe ratio, continuing to maintain the current visual tracking state; 6, applying the updated position model and the scale model to the next frame tracking; returning to the step 4.

Description

technical field [0001] The invention relates to the field of visual tracking of computer vision, in particular to a method for visual tracking through spatio-temporal context. Background technique [0002] Visual tracking is an important research hotspot in the field of computer vision, and it has been widely used in video surveillance, automatic driving, car navigation and human-computer interaction. The purpose of tracking is to accurately estimate the position of subsequent frames given the position state of the first frame. Although great development has been achieved in recent years, it still faces challenges from many external factors. For example, in the long-term tracking process, the target usually experiences some external disturbances, such as occlusion, illumination change, deformation, scale change and out of view, and these external disturbances will affect the accuracy of visual tracking. [0003] Tracking tasks are generally divided into position estimation...

AI Technical Summary

Problems solved by technology

[0004] Although the existing techniques achieve the desired tracking results and perform well for long-term tracking, when the target object undergoes complex appearance changes (such as severe occlusion) and disappears in the current frame, it will introduce some wrong background information and will be Passed to the next frame, long-term accumulation will deteriorate the quality of the tracking model and eventually cause tracking target drift

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