A Target Tracking Method Based on Sparse Discriminative Learning

Abstract

The invention discloses a target tracking method based on sparse discriminant learning, which includes: sampling the target and its surrounding background in the first frame to construct a target appearance model; extracting the two-dimensional image features of the target appearance model and performing normalization processing to obtain Initial dictionary; introduce a supervised discriminant dictionary learning method, increase the classification error item on the basis of the reconstruction error item, and train the discriminant dictionary; solve the minimum reconstruction error item under the sparsity constraint, and use the iterative exchange optimization strategy to update the dictionary and Sparse coding; Euclidean distance is used to measure the similarity between samples, and the sample with the highest similarity is used as the tracking target. Through the embodiment of the present invention, when establishing the appearance model of the target, the surrounding background that has a spatial correlation with the target is added as a clue template, and it is more robust to deal with the change of the target posture.

Description

technical field [0001] The present invention relates to the field of information technology, in particular to a target tracking method based on sparse discriminant learning Background technique [0002] Object tracking is one of the important basic problems in the field of computer vision research, and it has a very wide range of applications in monitoring, motion estimation, human-computer interaction, etc. Many tracking algorithms that have emerged in recent years can better track target objects in certain scenarios, such as particle filter, Boosting algorithm, L 1 tracking algorithm, etc. However, since the video is a sequence of sequential images in a complex scene, which includes illumination changes, occlusion, motion deformation, background clutter, target scale changes, etc., an adaptive target representation model is constructed to obtain robust tracking Algorithm is currently a research hotspot in the field of tracking, and it is also a difficult problem. [000...

AI Technical Summary

Problems solved by technology

Due to the introduction of noise templates, the algorithm is more accurate and robust when dealing with occlusions, but when the target is often deformed, the base vector of the template set cannot represent the deformed target

In addition, the template set is actually a dictionary without a learning process, so the sparse coding obtained cannot guarantee the maximum sparsity, and the sample with the smallest reconstruction error is not necessarily the best candidate, and it is easy to accumulate drift errors

Images