### Dynamic occlusion avoidance approach based on the depth image of moving visual object

KeyWords:Moving Object,Depth Image,Dynamic Occlusion Avoidance,Best View Model

#### Abstract:Dynamic Occlusion avoidance based on the depth image.

(Gaussian Curvature:The product of the maximum and minimum curvatures of the sectionsIt is the intrinsic measure of curvature.)

### Introduction

Next Best View:

• 用Octree模型来描述可视物体，然后对于不同观察情况的节点，给定不同的分数。
• 用大量周全的Candidate View
• 建立一个相似的参数模型，通过depth data和current fitted model来拟合
• 用B-spline计算信息增益来构建最佳NEXT VIEW
• 结合on-line theory来优化物体的3D重构
缺点：没有考虑遮蔽

### 三个问题：

1.如何解决动态遮蔽的避障问题? -> 用一个优化模型，结合运动预测在Best view Model里
2.如何通过深度图来预测可视物体的运动? -> 用两个高斯曲率特征矩阵的匹配来求R T,用到SIFT和SVD
3.如何有效评估动态遮蔽的影响? -> 用“effective avoidance rate”来评估算法的性能

### Method Overview

1.The analysis of dynamic occlusion avoidance

2.The overall idea of dynamic occlusion avoidance

### The Approach to dynamic occlusion avoidance based on depth image

1.Constructing the occlusion region to be avoided

2.Constructing the best view model

### Algorithm

step1:Calc the 3D pixels and the Gaussian Curvature Feature matrices
step2:Detect the Occlusion Boundary and establish the occlusion region in second depth
step3:Contruct the best view model
step4:Match the key points
step5:Solve the Objective formula
step6:Plan the next view of camera
step7:Acquire a depth image and calc the f(x)
step8:If the difference between two adjacent f(x) is less than a given threshold,then terminated,or jump to Step 4.

### Conclusions

1.Add emotion estimate in Best Next View to dynamic occlusion avoidance
2.Based the depth image to solve the R and T transformation
3.Propose the “Effective avoidance rate” to measure the performance of the algorithm.