自适应学习的时空上下文目标跟踪算法

Spatio Temporal Context Target Tracking Algorithm of Self-adaption Learning

传统时空上下文目标跟踪(STC)算法在目标发生尺度变化时,跟踪窗口长期不变导致学习的上下文空间模型不具有针对性。为此,提出一种能够进行自适应学习的时空上下文目标跟踪(STC-AL)算法。在前后输出窗口提取尺度不变特征并消除误匹配,对匹配点集进行综合分析后调整输出窗口,并对传统空间模型的学习与更新进行改进。实验结果表明,STC-AL算法能够适应目标尺度变化,与STC算法、CT算法和KCF算法相比,跟踪结果更准确。

For the traditional Spatio Temporal Context target tracking（STC） algorithm,no change of tracking’s window for a long time leads to learning space context model does not have a targeted problem when the target scale changes.This paper proposes a Spatio Temporal Context target tracking algorithm for Adaptive Learning（STC-AL）.The Scale Invariant Feature Transform（SIFT） is extracted from the front and back output windows and used to eliminate false matches.After analyzing the matching point set,the output window is adjusted,and the learning and updating of the traditional spatial model is improved.Experimental results show that STC-AL algorithm can adapt to changes in the target scale,and tracking is more accurate compared with that of STC algorithm,CT algorithm and KCF algorithm.