自适应多特征融合目标跟踪

Target Tracking Based on Adaptive Fusion of Multi-feature

针对目标跟踪在复杂场景中鲁棒性较差以及有效性较低的问题,基于在线检测跟踪框架提出一种基于回归的自适应多特征融合目标跟踪算法。对密集采样得到的各子图像块提取出多种特征分别建立目标表观模型,通过正则化最小二乘分类器得到各模型的响应,利用加权和准则融合各响应,通过求解岭回归方程自适应地在线更新各响应权重以增强局部判别力,得到精确而稳定的检测分数值,从而进行有效鲁棒地跟踪。实验结果表明,该算法在大多数复杂场景下其跟踪精度和鲁棒性优于现有的目标跟踪算法。

To solve the problem of poor robustness and low effectiveness of target tracking in complex scenes, a target tracking algorithm based on adaptive multi-feature fusion in tracking-by-detection framework is proposed. Features are extracted from the sub-images extracted by dense sampling, and the target appearance models are established respectively. The response of each model is obtained with regularized least squares classifier. The final response is achieved by weighted sums of the responses, in which the weights are updated by solving a regression equation. It helps to obtain accurate and stable detection scores by enhancing local discrimination. Experimental results show that the algorithm outperforms other state-of-the-art tracking algorithms in tracking accuracy and robustness in most complex scenes.