高維影像特徵於物件追蹤之研究

Object Tracking Using High Dimensional Descriptor and Nearest Neighbor Algorithms

康景翔(Ching-Hsiang Kang)

指導教授 ： 田仲豪 ； 戴亞翔

國立交通大學/電機學院/光電工程系所/碩士(2016年)

摘要

物件追蹤的技術是電腦視覺領域中一個相當重要的課題，而透過擷取影像中的局部特徵，並從中計算特徵描述，以此特徵描述實現的物件追蹤，可以達到穩健且快速。但由於特徵描述通常都是高維度的，因此需要設計不同的策略進行特徵描述搜索，以加速整體的追蹤效率。
在本研究中，分別介紹能夠偵測影像中尺度不變、旋轉不變的特徵，並生成64個維度特徵描述的SURF演算法，以及搭配均值聚類樹的優先搜索演算法，最後結合此二個演算法，實現一套物件追蹤系統，證實了相較於傳統的線性搜尋法，使用優先搜索演算法能夠使物件追蹤的每秒幀率上升約30%。

關鍵字

影像特徵 ； 物件追蹤 ； 估計最近鄰居

並列摘要

Object tracking is a challenging topic in computer vision. Local descriptors generated from distinctive image features can perform a robust and rapid object tracking. However, the feature descriptors are usually vectors in a high-dimensional space, causing the conventional linear search method inefficient.
In this research, two algorithms are introduced. The first one is called “SURF”, which is designed to detect the scale-and-rotation-invariant feature in a image, and then generate descriptors for each feature point. The second one is the priority search method combined with the K-means tree. This algorithm can perform fast searching especially for high-dimensional data such as SURF descriptors. In the experiment part of this study, it is showed that the priority search method can raise the object tracking FPS for about 30%, in contrast to conventional linear search.

並列關鍵字

image feature ； object tracking ； approximate nearest neighbors

參考文獻

[2] D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.

[4] S. Baker and I. Matthews, “Lucas-Kanade 20 Years On: A Unifying Framework,” International Journal of Computer Vision, vol. 56, no. 3, pp. 221-225, Feb. 2004.

[5] A. Bruhn, J. Weickert and C. Schnörr, “Lucas/Kanade Meets Horn/Schunck: Combining Local and Global Optic Flow Methods,” International Journal of Computer Vision, vol. 61, no. 3, pp. 211-231, Feb. 2005.

[6] P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, pp. I-511-I-518.

[7] H. Bay, A. Ess, T. Tuytelaars and L. V. Gool, “Speeded-Up Robust Features (SURF),” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346-359, Jun. 2008.