摘要
TLD(Tracking-Learning-Detection)算法是近期受到广泛关注的一种长时间视觉跟踪算法.为提高该算法的运行速度,一种ATLD(Accelerated TLD)算法被提出,对原始TLD算法做了两方面改进:在检测模块引入基于灰色预测模型的目标位置估计和检测区域设置;运用基于NCC(Normalized Cross Correlation)距离的图像检索方法管理正负样本集.并在此基础上实现了多目标跟踪.通过实验比较了ATLD算法、原始TLD算法及两种近期改进的TLD算法.实验结果表明:ATLD算法在确保精度的前提下运行速度更快.
Tracking-Learning-Detection（TLD） is a kind of long-term visual tracking algorithm which receiveds wide attention in recent years. In order to improve the running speed of this algorithm, a novel algorithm named Accelerated TLD（ATLD） is proposed in this paper. Two aspects of improvements were made in original TLD algorithm. The improvement includes as follows： using a grey prediction model in the detection module for estimating the location of the target and setting a detection area; applying an image indexing method based on normalized cross correlation（NCC） distance to manage the positive and negative sample set. And on this basis, the multiple targets tracking algorithm is realized. Through experiments, the ATLD algorithm, the original TLD algorithm and other two recent improved TLD algorithm are compared. The experimental results show that the ATLD algorithm runs faster on the premise of ensuring the accuracy.
出处
《计算机系统应用》
2016年第6期196-201,共6页
Computer Systems & Applications
基金
国家自然科学基金(61373063)
关键词
目标跟踪
TLD
目标位置估计
图像检索
target tracking
tracking-learning-detection
target location prediction
image index
