摘要
为了应对运动目标跟踪任务中目标的尺度、光照变化和形变等情况,提出了一种基于混合特征的运动目标跟踪方法——So H-DLT,综合考虑了运动目标的轮廓特征和细节特征.在粒子滤波跟踪过程中引入方向直方图描述目标轮廓特征,保证与目标最相似的粒子在尺度、光照变化和形变的情况下仍能获得较高的置信度,并作为跟踪结果输出.结合深度学习获得的高层特征和具有尺度不变性的加速鲁棒特征计算粒子权重,提高了复杂运动场景下目标跟踪的准确度,强化了So H-DLT方法对尺度变化运动目标跟踪的鲁棒性.实验结果表明,So H-DLT与其他方法相比获得了更好的跟踪效果.
In order to deal with scale variation,illumination changes and deformation of the target in tracking tasks,a visual tracking algorithm based on mixed features,called So H-DLT,was proposed,considering both the contour features and detail features. Orientation histogram is introduced to describe the contour features of candidate samples in the process of particle filter,ensuring that the particle which is the most similar with the target can still get a high degree of confidence and can be output as the result of tracking in the case of scale,illumination changes and deformation. Speed-up robust features( SURF)feature and high-level features from deep learning are integrated to calculate the weights of particles,improving the tracking accuracy in complex scenes and enhancing the robustness of So H-DLT to scale variation. Experiments show that So H-DLT algorithm has better tracking performance than the contrast algorithms in both quantitative and qualitative evaluation.
作者
任楠
杜军平
朱素果
李玲慧
Jang Myung Lee
REN Nan DU Jun-ping ZHU Su-guo LI Ling-hui JangMyung Lee(School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China Department of Electronics Engineering, Korea Pusan National University, Busan, Korea)
出处
《北京邮电大学学报》
EI
CAS
CSCD
北大核心
2016年第6期88-92,98,共6页
Journal of Beijing University of Posts and Telecommunications
基金
国家自然科学基金项目(61320106006
61532006
61502042)
关键词
运动目标跟踪
轮廓特征
神经网络
方向直方图
加速鲁棒特征
粒子滤波
visual tracking
contour feature
neural network
orientation histogram
speed-up robust features
particle filter