CAJ | 학술논문

参考目标模型中混入的背景噪声会弱化目标特征的描述，导致目标跟踪定位误差。为减少误差，依据目标与背景处于不同深度平面的特点，提出了基于深度信息辅助的和改进的背景加权直方图的Mean Shift跟踪算法，能够有效削弱核窗口中的背景干扰信息，突出目标的颜色特征信息，并适时自适应更新核带宽，减少因目标尺寸变小时引入较多的背景干扰信息。实验结果表明该算法迭代次数更少，具有良好的跟踪定精度。
삼고목표모형중혼입적배경조성회약화목표특정적묘술，도치목표근종정위오차。위감소오차，의거목표여배경처우불동심도평면적특점，제출료기우심도신식보조적화개진적배경가권직방도적Mean Shift근종산법，능구유효삭약핵창구중적배경간우신식，돌출목표적안색특정신식，병괄시자괄응경신핵대관，감소인목표척촌변소시인입교다적배경간우신식。실험결과표명해산법질대차수경소，구유량호적근종정정도。
The background noise in the candidate object model diminishes the object color characteristic, and induces localiza-tion error. To reduce the error, according to the discriminative depth level between the object’s and the background’s, a Mean Shift algorithm based on depth cues assisted and corrected background-weighted histogram is proposed. The proposed algorithm can sufficiently weaken the background noisy interference in the kernel window, enhance the object’s color feature information, and update the kernel size adaptively in due course to reduce the distractive information in the background as the object size becomes small. Experimental result shows the proposed algorithm has fewer iteration number and good localization precision of tracking.