DSPCA在自适应视频跟踪算法中的应用

Adaptive visual tracking based on DSPCA

为了解决单一固定目标模型在复杂的场景中易产生跟踪漂移问题,提出一种基于DSPCA的自适应粒子滤波跟踪方法,通过稀疏主成分分解(DSPCA)在线获取互补图像集,同时将其按照新的相似度BRS进行自适应融合作为新目标模型。与经典的粒子滤波跟踪算法、视觉分解跟踪算法和多特征自适应融合跟踪算法,与有挑战性较高的场景视频相比,提出的算法在形态、运动快速及严重遮挡的运动场景中,都能鲁棒地跟踪到目标。

In order to solve the problem resulted from tracking drift of single reference color histograms ,a sort of particle filtering algorithm based on DSPCA （decomposition-sparse-principal component-analysis） is presented .The image set with complementary is accessed by DSPCA ,which is fused by means of the new similarity as the new object model .Compared with the basic particle filtering ,visual tracking decomposition and adaptive multiple cues fusion tracking algorithm in challenging scenarios ,the presented method can robustly track the object in fast motion and the object w hich is mostly blocked .