融合颜色信息与深度信息的运动目标检测方法

基于颜色信息的运动目标检测易受光照、阴影等影响，基于深度信息的运动目标检测存在目标边缘噪声大，无法检测距离背景较近的目标等问题。针对上述问题，该文利用CCD相机获取的颜色信息及TOF相机获取的深度信息分别为每个像素建立颜色与深度信息的分类器，根据像素点的深度特征及前一帧的检测结果，自适应地为每个分类器的输出分配不同的权值，实现运动目标的检测。该文采集多组视频序列进行实验，实验结果表明该方法能有效解决单独利用颜色或深度信息进行运动目标检测时出现的问题。

Moving Object Detection Based on the Fusion of Color and Depth Information

Color-based moving object detection performs poorly when illumination changes or shadow exists. Depth-based moving object detection is affected by the high level of depth-data noise at object boundaries, and it fails when foreground objects move close to the background. For these reasons, a novel approach that establishes color and depth classifier for each pixel is presented by making full use of color information obtained by CCD camera and depth information obtained by TOF camera. In order to realize the effective detection, different weights are assigned adaptively for each output of the classifier by considering foreground detections in the previous frames and the depth feature. Multi video sequences are captured to verify the proposed method, and the experimental results show that the proposed approach can effectively solve the limitations of color-based or depth-based detection and realize the effective detection.