计算机与现代化 ›› 2013, Vol. 1 ›› Issue (9): 91-94.doi: 10.3969/j.issn.1006-2475.2013.09.022

• 图像处理 • 上一篇    下一篇

基于图像和深度信息融合的目标跟踪

马 俊   

  1. 北京工业大学计算机学院,北京 100124
  • 收稿日期:2013-04-08 修回日期:1900-01-01 出版日期:2013-09-17 发布日期:2013-09-17

Object Tracking Algorithm Based on Image and Depth Fusion

MA Jun   

  1. College of Computer Science, Beijing University of Technology, Beijing 100124, China
  • Received:2013-04-08 Revised:1900-01-01 Online:2013-09-17 Published:2013-09-17

摘要: 大多数目标跟踪都是基于视频图像序列中目标的跟踪,但在跟踪过程中可能会受到光照变化和复杂背景的影响,从而导致跟踪结果的不稳定。为了获得稳定的跟踪结果,在跟踪中引入深度信息。考虑到深度相机采集的深度信息不随场景中光照或颜色的变化而变化,设计基于颜色图像和深度信息融合的目标跟踪算法。最后结合粒子滤波跟踪框架,确定当前选取目标区域的中心和大小来对目标进行跟踪。由于引入了深度信息,在跟踪的同时,可以同步得到目标的运动轨迹。实验结果表明,目标的颜色和深度联合特征具有较强的鲁棒性,在光照变化和同色干扰情况下也获得了很好的稳定性,能实现复杂场景下的目标跟踪。

关键词: 目标跟踪, 深度相机, 粒子滤波

Abstract: Most object tracking are based on video sequence, visual object tracking is often disturbed by illumination change and the clutter background, it makes the result is of poor robustness. In order to obtain stable tracking results, this paper uses depth data for tracking. Taking into account the stability of the depth data, it does not vary with the illumination or color change, in this paper, we design an object tracking algorithm based on color and depth data fusion. To track object, an integrated particle filtering method is implemented. Due to use depth data in tracking, it shows the trajectory of the object at the same time. Experimental results show that this fused formation algorithm can achieve strong robustness, and have stability result from illumination change and similar color, and also exactly track target in complex scene.

Key words: object tracking, depth camera, particle filtering

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