融合深度特征的改进KCF行人追踪算法

An improved KCF mixed with deep feature for pedestrian tracking

KCF(kernel correlation filter)算法是目前追踪效果最好的算法之一,但KCF算法无尺度自适应能力且不能有效解决遮挡问题.针对KCF算法的缺陷,设计了1种改进的KCF模型,IKCFMDF(improved KCF mixed with deep feature).该模型先由KCF算法得到目标行人的预测框,然后与通过目标检测得到的所有行人检测框相比较,将交并比最大的检测框作为新的训练目标,从而实现KCF的尺度自适应;引入深度特征代替原KCF使用的HOG(histogram of oriented gradient)特征,以避免HOG特征对行人的姿势改变和颜色信息不敏感的缺陷;在追踪目标丢失时保留其深度特征,与之后视频中行人的深度特征进行对比,如果相似度大于设定阈值,则可判定目标重新出现,从而有效解决KCF的遮挡问题.与原始KCF相比,改进的模型在复杂情况下,对行人追踪的成功率提升了36%,相比主流的神经网络模型也有少量的提升.

The KCF(kernel correlation filter)algorithm is currently one of the best tracking algorithms,but it has no scale adaptability and cannot solve the occlusion problem.To solve the problems of the KCF algorithm,an improved KCF model,named IKCFMDF(improved KCF mixed with deep feature),is proposed.This model obtains the prediction frame of the target pedestrian by the KCF algorithm,and then compares it with all the pedestrian detection frames obtained through target detection method,and uses the detection frame with the largest intersection-over-union as the new training target,thus achieving the scale adaptation of KCF.By introducing the deep features instead of the HOG features of KCF,the algorithm can avoid the defects that HOG(histogram of oriented gradient)features are insensitive to pose changes and color information of pedestrians.The deep feature is retained when the tracking target is lost,and then the deep feature is compared with the pedestrians in the subsequent videos.If the similarity is greater than the set threshold,it can determine that the target reappears,thereby the occlusion problem of KCF is solved.Compared with the original KCF,the success rate of pedestrian tracking in complex situations is increased by 36%,and compared with the popular neural network models,the proposed model also achieves improvement.