Visual　tracking　is　a　key　problem　for　many　computer　vision　applications　such　as　human-computer　interaction,　intelligent　medical　diagnosis,　navigation　and　traffic　control　management.　Most　of　the　existing　tracking　methods　are　mainly　based　on　correlation　filters.　However,　boundary　effect,　scale　estimation　and　template　updating　have　not　been　fully　resolved.　Herein,　this　paper　presents　a　new　hierarchical　tracking　method　combining　structural　correlation　filters　with　a　Gaussian　Particle　Filter　(GPF),　named　KCF-GPF.　Weak　KCF　classifiers　are　constructed　via　a　Lukas-Kanade　(LK)　method　and　the　preliminary　target　location　is　presented　as　a　weighted　sum　of　these　classifiers.　Specially,　a　facile　weight　strategy　is　implemented　to　estimate　the　reliability　of　each　weak　classifier.　On　the　basis　of　the　preliminary　target　location,　the　GPF　using　features　from　a　Convolutional　Neural　Network　(CNN)　is　employed　to　predict　the　location　and　scale　of　a　target.　Extensive　experiments　with　the　OTB-2013　and　the　OTB-2015　databases　demonstrate　that　the　proposed　algorithm　performs　favourably　against　state-of-the-art　trackers.　©　2020