本文提出一种新的融合 SIFT(尺度不变特征 )和压缩特征的目标跟踪算法以解决姿态变换、光照变化、旋转和运动模糊下目标的稳定准确跟踪问题。算法使用压缩特征对目标和背景进行描述, 通过在图像帧中采集到的正负样本在线训练和学习 SVM(支持向量机 )分类器, 将跟踪任务构建为一个二类分类问题。使用该分类器对下一帧的目标和背景进行分类, 从而获得精确的目标位置和区域。同时, 算法使用前后两帧的 SIFT特征点之间的对应匹配关系求解目标尺寸变化值, 实现模板大小的自适应调整。将算法与其他算法在某些图像序列上的跟踪比较显示, 该算法在有效性、正确性和鲁棒性上性能优越。

An algorithm based on SIFT and compressive features is proposed to develop effective and efficient appearance models for robust object tracking due to factors such as pose variation, illumination change, occlusion, and motion blur. The algorithm describes the target and background with compressive features which labeled as positive and negative specimens sampling from frames. The tracking task is formulated as a binary classification via a SVM classifier with online update in the compressed domain. In new frame, utilize the classifier to obtain the target’s position. Meanwhile, introduce SIFT to solve the target size change, so as to achieve adaptive template size. The proposed tracking algorithm performs favorably against state-of-the-art algorithms on challenging sequences in terms of efficiency, accuracy and robustness.