基于自适应多模型联合的目标跟踪算法

Target Tracking Algorithm Based on Adaptive Multi-Model Joint

基于核相关滤波器的跟踪算法对于目标的空间结构具有较强的依赖性,无法有效应对遮挡、形变等干扰因素,且单一的特征模型在复杂的跟踪场景下无法准确表述目标信息。为此,提出一种基于自适应多模型联合的算法。通过自适应权重将相关滤波模型与颜色直方图模型进行联合,并将稀疏表示的思想引入相关滤波模型的训练过程中,以增强算法的鲁棒性。OTB视频序列数据集上的实验结果表明,该算法可有效缓解跟踪过程中的遮挡、形变等因素的干扰,与Staple算法、KCF算法相比,目标跟踪的精度显著提升。

The tracking algorithm based on Kernelized Correlation Filter(KCF) has strong dependence on the spatial structure of the target,cannot effectively deal with the occlusion,deformation and other interference factors,and the single feature model is unable to accurately represent the target information in complex tracking occasions.Therefore,an adaptive multi-model tracking algorithm is proposed.The correlation filter model and the color histogram model are combined by adaptive weight,and the idea of sparse representation is introduced into the training process of the correlation filter model to further enhance the robustness of the algorithm.Experimental results on the OTB dataset show that the algorithm can effectively mitigate the interferences from various factors such as occlusion and deformation in the tracking process,and compred with Staple algorithm,KCF algorithm and others,the algorithm achieves significant improvement in target tracking accuracy.