運用立體視覺系統於三維特徵點之追蹤及定位研究

近幾年來，隨著影像處理和計算機視覺的進步，影像處理的技術已不僅僅侷限於單張影像(或稱為平面視覺)。模擬人類視覺取得深度資訊能應用在許多領域上，因此使用兩部攝影機之立體視覺系統已逐漸成為令人感興趣的研究。在本研究中，我們建立一套系統能夠在左邊視訊及右邊視訊中之三維特徵點進行追蹤及定位。本系統方法包括特徵點定義、特徵點追蹤、特徵點定位及深度計算。此外，我們使用了幾種研究參數去評估深度計算的誤差率，包涵幾種不同深度、平面視覺追蹤或立體視覺追蹤、是否使用核函數及使用整數像素精確度或次像素精確度。結果證明本系統在立體視覺中，能夠正確的追蹤特徵點及定位，並且取得適當的深度資訊。此外，從結果中可以看出立體視覺追蹤及使用次像素精確度明顯的比平面視覺追蹤及使用整數像素精確度好。總結而言，本系統提供追蹤三維特徵點並定位之可能解決方法，未來可與其他視訊應用結合。

關鍵字

立體視覺；特徵追蹤；深度資訊；計算機視覺；次像素精確度

並列摘要

With the advances of image processing and computer vision, techniques being developed are no longer limited in images acquired with single camera (namely plane vision). A stereo vision system with two cameras has become the research of interest in many areas because its ability to yield the depth information similar to human vision. In this study, the objective was to develop a system that can automatically track and localize a 3D feature in motion using left and right video sequences. The system design included feature definition, feature tracking, feature localization, and depth computation. In addition, we evaluated our system with several research parameters, including various depths, video tracking in plane vision or stereo vision, use of kernel functions, and integer-pixel vs. sub-pixel accuracy. Our results demonstrated that the system could track and localize the given feature in motion, leading to yield reasonable results of depth information. In addition, the video tracking in stereo vision with sub-pixel accuracy clearly outperformed the video tracking in plane vision with integer-pixel accuracy. In summary, our system yielded a potential solution in tracking and localizing 3D feature that could be incorporated in a large variety of video applications.

並列關鍵字

feature tracking ； stereo vision ； sub-pixel accuracy ； depth information ； Computer vision

參考文獻

[1] S. Bahadori, L. Iocchi, G. R. Leone, D. Nardi and L. Scozzafava, “Real-time people localization and tracking through fixed stereo vision,” Applied Intelligence, Vol. 26, No. 2, pp. 83-97, 2007.

[5] M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” Lecture Notes in Computer Science, 1996 – Springer.

[6] R. T. Collins, “Mean-shift blob tracking through scale space,” Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2003.

[7] D. Comaniciu and P. Meer, “Mean shift: A robust approach toward feature space analysis,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002.

[8] D. Comaniciu and V. Ramesh, “Real-time tracking of non-rigid objects using mean shift,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, vol. 2, pp. 142-149, June 2000.

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運用立體視覺系統於三維特徵點之追蹤及定位研究

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