CAJ | 학술논문

通过分析经典稀疏视觉跟踪算法在粒子滤波框架下的采样粒子分布与运动目标真实状态的差异，提出了一个基于在线判别分析的改进稀疏视觉跟踪算法。该跟踪算法通过在线逻辑斯蒂判别分析模型及其更新过程，自主获取运动目标的实时状态与变化，增强运动目标与背景信息之间的可判别性。同时，实现对采样粒子的预先筛选，尽量排除与运动目标差异大的粒子，以提高跟踪算法的鲁棒性，同时减少L1优化求解的次数从而提高算法的执行效率。与5个高水平跟踪算法在4段公开视频上的实验结果表明，提出的算法能够长时间鲁棒地对运动目标进行跟踪，同时相对典型稀疏跟踪算法而言，明显地降低了计算复杂度。
통과분석경전희소시각근종산법재입자려파광가하적채양입자분포여운동목표진실상태적차이，제출료일개기우재선판별분석적개진희소시각근종산법。해근종산법통과재선라집사체판별분석모형급기경신과정，자주획취운동목표적실시상태여변화，증강운동목표여배경신식지간적가판별성。동시，실현대채양입자적예선사선，진량배제여운동목표차이대적입자，이제고근종산법적로봉성，동시감소L1우화구해적차수종이제고산법적집행효솔。여5개고수평근종산법재4단공개시빈상적실험결과표명，제출적산법능구장시간로봉지대운동목표진행근종，동시상대전형희소근종산법이언，명현지강저료계산복잡도。
By analyzing the distribution of the particles sampled from particle filter and their difference with the true states of the moving target, a robust sparse tracking method based on online logistic discriminate learning is proposed. The discrimination of the moving target and backgrounds is enhanced by the discriminating and updating procedures of the logistic function. The proposed method is more efficient than the original sparse tracking since the particles with little relevancy are rejected in advance of the L1 optimization. Experimental results demonstrate that, with 5 state-of-the-art tracking under 4 challenging video sequences, the proposed tracking method obtains the most robust results, and its run-ning time is almost equivalent with the BPR-L1 tracking method.