In　multi-target　tracking,　the　target＇s　scale　change　and　occlusion　problems　easily　lead　to　target　loss.　At　the　same　time,　multi-target　tracking　algorithm　of　data　association　based　on　deep　learning　for　target　detection　requires　a　high　hardware　platform　and　cannot　run　on　a　multi-core　DSP.　Aiming　at　the　above　problems,　this　paper　proposes　an　online　multi-target　tracking　algorithm　based　on　improved　kernel　correlation　filter,　using　a　multi-core　parallel　mechanism　to　establish　a　tracker　for　each　target　for　tracking.　The　improved　algorithm　integrates　multiple　features　of　Hog,　CN　and　LBP　at　the　feature　level　to　enhance　the　ability　to　represent　the　target;　for　the　problem　of　target　scale　change,　a　1D　scale　filter　is　introduced　to　estimate　the　new　scale　of　the　target;　In　terms　of　model　updating,　the　APCE　indicator　is　used　to　measure　the　confidence　of　the　response　map　in　order　to　detect　whether　the　target　is　occluded　or　lost　in　time.　By　adopting　a　high-confidence　model　update　method,　the　correlation　filter　model　is　prevented　from　being　polluted　by　the　wrong　samples　when　the　target　is　occluded.　When　the　target　tracking　fails,　the　detection　module　is　triggered　to　detect　the　target:　firstly,　the　correlation　matching　algorithm　based　on　the　hog　feature　is　used　to　preliminarily　screen,　and　the　relevant　surface　is　analyzed,　and　then　the　kernel　correlation　filter　based　on　the　ridge　regression　classifier　is　used　to　detect　the　multi-peak　of　the　correlation　surface.　Experimental　results　show　that　the　improved　algorithm　is　more　robust　to　target　occlusion,　scale　changes,　background　interference,　target　loss　re-tracking,　etc.　compared　to　standard　algorithms.　This　paper　effectively　extends　the　improved　kernel　correlation　filter　algorithm　to　multi-target　tracking,　which　can　be　further　transplanted　to　multi-core　DSP　platforms.　©　2020　IEEE.