CAJ | 학술논문

针对压缩感知算法中的低维特征对目标重构效果较差的问题，提出基于自适应压缩特征选择的目标跟踪算法。该算法首先提取满足目标重构要求的高维压缩特征，再通过所提出的特征选择方法选择区分度高的低维特征作为目标的外观模型，从而降低计算复杂度。为自适应选择特征，采用一种差分方法控制特征维数，满足实时性要求。实验表明，与其他算法相比，文中算法具有更强的鲁棒性和实时性。
침대압축감지산법중적저유특정대목표중구효과교차적문제，제출기우자괄응압축특정선택적목표근종산법。해산법수선제취만족목표중구요구적고유압축특정，재통과소제출적특정선택방법선택구분도고적저유특정작위목표적외관모형，종이강저계산복잡도。위자괄응선택특정，채용일충차분방법공제특정유수，만족실시성요구。실험표명，여기타산법상비，문중산법구유경강적로봉성화실시성。
Low dimensional features adopted by compressive tracking algorithm can not reconstruct the object effectively. To solve this problem, a real-time object tracking algorithm based on adaptive compressive feature selection is proposed in this paper. The high dimensional features meeting the requirement of object reconstruction are extracted. Then the lower dimensional features with a higher discrimination are selected as appearance model of the object to reduce the computational complexity. To select features adaptively a difference method is adopted to control the feature dimensionality. The experimental results demonstrate that the proposed algorithm are more robust and effective in real time than other state-of-the-art tracking algorithms.