Resumen de Computer vision recognition and tracking algorithm based on convolutional neural network
Yujie Tian, Di Cao
In the past few decades, target tracking algorithm has been paid great attention by peers at home and abroad in the field ofcomputer vision because of its potential for in-depth research and practical value. Typical applications of target trackingalgorithms include intelligent video surveillance, autonomous vehicles, human-computer interaction and so on. Given theinitial state of a target object, the task of the target tracking algorithm is to estimate the state of the target in the subsequentvideo. Despite years of efforts, designing a target tracking algorithm is still a very challenging problem, because it poseschanges, particularly illumination changes, and in addition, occlusion, complex environments, and the moving backgroundwill also cause changes in the appearance of the target. The traditional target tracking algorithm based on manuallydesigned features or shallow classifiers uses manually designed low-level visual features or shallow classifiers to build thetarget apparent model, so the semantic information prediction ability of the target apparent model is limited. Given thedefect that the above traditional target tracking algorithm is difficult to capture the semantic information of visual data inthe target apparent model, inspired by the great success of deep convolution networks in image classification and speechrecognition, a target tracking algorithm based on convolution neural network is proposed in this paper.
