Research on Target Loss Warning Method of KCF Algorithm Based on Outlier Detection
Aiming at the problem that the KCF algorithm cannot detect the loss of targets, this paper proposes a target loss warning mechanism based on the outlier detection method. This method detects the peak value of the response of each frame. If the abnormal peak value is found, it can make a judgement that the target is lost or is about to be lost, and the problem that target loss cannot be detected is solved. Experimental results show that the method proposed in this paper can correctly warn when the target in the KCF algorithm tracking process is lost, the success rate reached 100%, It has high reliability and provide a reliable basis for loading target re detection and location mechanism after the target is lost. INTRODUCTION Visual object tracking is one of the most challenging subjects in the field of computer vision. It is widely used in video surveillance, human-computer interaction, intelligent automobile, medical images and other fields 1,2 . The research of visual object tracking has been developed for more than 30 years since 1980s 2 ,has made great achievements, and a large number of tracking algorithms have emerged. Nevertheless, there is still not a tracking algorithm can adapt to any natural scene, illumination variation, occlusion, deformation, out of view and other issues still are the main factors influencing the performance of tracking algorithms, which are also difficult problems to break through in the field of tracking. The correlation filter tracking method 3-8 is currently a popular research direction. Henriques 9 proposed the tracking cycle structure detection (CSK) algorithm, the algorithm using the cyclic matrix can be diagonalized Fourier properties will calculate classifier training and target detection is converted to the frequency domain on the fast calculation. Henriques 10 proposed kernel correlation filter (KCF) based on CSK tracking algorithm using histogram of oriented gradient (Hog) 11 features of to replace the original gray value feature, the correlation filter by single channel extended to multiple channels, to enhance the tracking performance. Many scholars have improved the KCF algorithm, and the tracking performance has been improved. But when the target is affected by serious occlusion, similar target interference and out of view, the KCF algorithm will still suffer from the phenomenon of target loss. Once the target is lost, it is very difficult to recover the target tracking, In document of 12, the peak sidelobe ratio (PSR) is proposed Yan-fei LIU 1 Yan-hui HE 1 Peng-tao ZHAO 1 Zhong WANG 1 Yan-fei LIU, Yan-hui HE, Peng-tao ZHAO, Zhong WANG, Xi’an Research Institute of High Technology, Xi’an, China Corresponding author: 549838183@qq.com