Abstract: In an effort to solve the loss of target tracking in complicated movements, a target tracking algorithm with Gaussian-aware constraint space anomaly is proposed. Firstly, the feature sampling points of the target are established with Gaussian uniform distribution as the distribution law, and the appearance model and weight model of the target are extracted with convolution structure. Secondly, in an effort to constrain spatial anomaly, spatial regular terms are constructed in the target function; while at the same time the target weight model is updated to minimize the occurrence of spatial overfitting, thereby enhancing the spatial anomaly adaptability of the tracker. Lastly, the weighted least square method is applied to obtain the weight response model center, so as to determine the target center, update the tracking position, thereby enhancing the robustness of the tracker. By means of OTB2015 and UAV20L dataset, the algorithm proposed in this paper, when compared with other mainstream relevant filtering algorithms, presents high tracking success rate and tracking accuracy under such complicated circumstances as low resolution and obstruction due to target motion.

Key words: machine vision, constraint space exception, space regularity, correlation filtering

摘要： 针对目标在复杂场景运动过程中容易出现跟踪丢失问题，提出一种高斯感知约束空间异常的目标跟踪算法。以高斯均匀分布为分布规律建立目标特征采样点，采用卷积结构提取目标的外观模型以及权重模型；为了约束空间异常，在目标函数中构建空间正则项，同时更新目标权重模型，减小空间过拟合的产生，增强跟踪器的空间异常适应性；应用加权最小二乘法思想，获得权重响应模型中心，确定目标中心，更新跟踪位置，增强跟踪器鲁棒性。使用OTB2015和UAV20L数据集，与其他主流相关滤波算法相比，该算法在目标运动导致低分辨率、遮挡等复杂条件下，跟踪成功率以及跟踪精度较高。

关键词: 机器视觉, 约束空间异常, 空间正则, 相关滤波