Scale Self-Adaption Tracking Method of Defog-PSA-Kcf Defogging and Dimensionality Reduction of Foreign Matter Intrusion along Railway Lines

Abstract

In foggy weather, the occlusion of smoke dust and the variation of imaging scale of foreign objects will mislead the existing algorithm to learn the wrong target information, causing the drift of the tracking box. A kernel correlation filtering target tracking dimensionality reduction algorithm combining dark channel prior and scale estimation is proposed, effectively improve target tracking accuracy in foggy weather. Firstly, in the process of detecting intruding foreign objects along the railway with visual background extractor ViBe, the atmospheric scattering model based on dark channel prior was used to defog video sequence. After that, FHOG features of the initial tracking box were extracted by dense cyclic sampling and scale pyramid technology, and then a Kcf position filter, as well as a scaling filter with PCA dimensionality reduction was trained to realize the scale-adaptive rapid tracking of railway foreign objects in foggy weather. The experimental results show that in terms of tracking accuracy, the proposed Defog-PSA-Kcf algorithm is superior to the non-scale estimation link the generation algorithm Mean Shift devoid of scale estimation and the native Kernel Correlation Filter algorithm (Kcf), and while linear kernel algorithm Dual Correlation Filter algorithm (Dcf), which is higher than the scale-adaptive SA-Kcf and the SAMF algorithm; in terms of tracking speed, it's faster than the Mean Shift, SA-Kcf, and SAMF algorithms, and in tracking speed. The algorithm can achieve fast tracking results comparable to as fast as the Kcf algorithm.