融合视觉深度的特征计算与水下目标跟踪

目的：受水下复杂光学环境以及水下运动目标特性影响，水下视频图像中难以获取准确的目标特征，也难以准确预测目标空间尺寸，使得目标跟踪过程中跟踪窗偏移量较大且无法准确地包络目标区域。本文提出一种新的以视觉深度信息为核心的目标特征计算和跟踪方法。方法：首先，基于暗原色先验计算视觉深度信息，提取目标的空间位置特征；然后，基于深度信息对水下图像进行去光幕及色彩恢复，增强图像目标特征，最后，在贝叶斯滤波框架下对水下目标进行跟踪，同时结合目标深度信息及尺度变化规律自适应调整跟踪窗口大小。结果：实验结果表明，本文提出的方法能够根据视觉深度信息准确计算目标特征并优化跟踪窗口，实现对水下目标的自适应跟踪。结论：本文提出了一种新的水下目标跟踪方法，以视觉深度信息计算为核心。实验结果验证了该方法在水下目标自适应跟踪方面的鲁棒性，可适用于各种非线性非高斯水下目标跟踪框架中。

Visual depth based feature calculation and underwater target tracking

Objective Because of the complicated optical environment underwater and the moving target characteristics,it is difficult to extract target features and predict target sizes precisely in underwater videos.Thus, tracking window offsets become bigger and cannot envelop the target area accurately during the target tracking process.A novel approach of visual depth based target feature calculation and target tracking is therefore presented.Method First,visual depth information is calculated by dark channel prior,thus the target's spatial position feature is extracted.Second,dehazing and color restoration of the underwater image is applied based on the depth information and the target's feature will be enhanced.At last, an underwater target is tracked under the Bayesian filter framework.Meanwhile,the target window size is adaptively adjusted based on the target's spatial position feature.Result Experimental results show that the proposed algorithm can calculate target features and optimize tracking windows based on the visual depth.Thus, objects can be tracked adaptively.Conclusion This paper presents a new underwater targets tracking method based on the calculation of visual depth information.Experimental results validate its robustness in underwater target adaptive tracking. Furthermore, it can be used in various nonlinear non-Gaussian underwater target-tracking frameworks.