CAJ | 학술논문

为处理目标的消失重现、形变及环境变化等问题，要求跟踪算法有一定的检测与学习能力。针对全局检测方法因冗余检测而造成检测效率低下的问题，在基于 P-N 学习的跟踪框架的基础上，提出一种自适应生成检测范围的目标跟踪算法。通过引入卡尔曼滤波器(Kalman filter)对目标位置、尺度以及两者的变化速度进行预估，在检测前根据预估信息自适应生成检测范围，提高检测效率。在公开的 CoGD 数据集上进行实验，结果证明该算法较原始算法在准确度基本不变的基础上，速度得到显著改善。
위처리목표적소실중현、형변급배경변화등문제，요구근종산법유일정적검측여학습능력。침대전국검측방법인용여검측이조성검측효솔저하적문제，재기우 P-N 학습적근종광가적기출상，제출일충자괄응생성검측범위적목표근종산법。통과인입잡이만려파기(Kalman filter)대목표위치、척도이급량자적변화속도진행예고，재검측전근거예고신식자괄응생성검측범위，제고검측효솔。재공개적 CoGD 수거집상진행실험，결과증명해산법교원시산법재준학도기본불변적기출상，속도득도현저개선。
Visual object tracking is a highly challenging task due to the fact that it suffers from many intrinsic problems, such as object disappearance, reappearance, deformation and environment variation. A tracking algorithm is required to have the ability of detecting and learning. Given the fact that the global detector would generate certain redundant detections, we propose an approach that adaptively generates the detective range for visual object tracking based on the framework of P-N learning. We introduce a Kalman filter to predict the location, the object scale as well as their changing rate. Then we utilize the predicted information to reduce the scope of detecting. Our experimental results on the pubic CoGD dataset show that our method increases the speed dramatically while without compromising the accuracy.