A Stochastic Fern Target Tracking Method Based on Deep Residual Network

Abstract

The invention discloses a random fern target tracking method based on a deep residual network, and relates to the technical fields of computer vision and pattern recognition. The target object to be tracked is selected and determined from the initial image, and the detector is constructed and initialized; the image sequence is composed and the frame images are extracted one by one as the input image in chronological order; during the tracking process, the short-term tracker is at the target position determined last time. Compare the search area centered with the target image block; extract the target image block as a positive sample, and select a background image block around it as a negative sample, generate an online training set and input it to the detector; Perform target detection, compare the target probabilities of all test image blocks, take the position corresponding to the test image block with the largest target probability as the position of the target, and the target positioning is completed.

Description

technical field [0001] The invention relates to the technical fields of computer vision and pattern recognition. Background technique [0002] Visual object tracking is an important research topic in the field of computer vision. Its main task is to obtain the continuous position, appearance and motion information of the object, and then provide the basis for further semantic analysis (such as behavior recognition, scene understanding, etc.). Target tracking research is widely used in intelligent monitoring, human-computer interaction, automatic control systems and other fields, and has strong practical value. At present, target tracking methods mainly include classical target tracking methods and deep learning target tracking methods. [0003] The classic target tracking methods are mainly divided into two categories: Generative Methods and Discriminative Methods. The generative method assumes that the target can be expressed through some kind of generative process or mod...

AI Technical Summary

Problems solved by technology

The research on deep learning object tracking is also developing very rapidly, but due to the lack of prior knowledge and real-time requirements in object tracking, it is difficult to fully display the deep learning technology based on a large amount of training data and parameter calculation. a lot of room to explore

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