Based on the combined shape feature and texture
feature, a fast object detection method with rotation invariant features
is proposed in this paper. A quick template matching scheme based
online learning designed for online applications is also introduced in
this paper. The experimental results have shown that the proposed
approach has the features of lower computation complexity and
higher detection rate, while keeping almost the same performance
compared to the HOG-based method, and can be more suitable for
run time applications.
 N. Dalal and B. Triggs. Histograms of Oriented Gradients for Human
Detection. In CVPR, 2005.
 M.Grabner, H.Grabner, and H.Bischof. Semi-supervised on-line
boosting for robust tracking. In ECCV, 2008.
 Timo Ojala, Matti and Topi. Multiresolution Gray-Scale and Rotation
Invariiant Texture Classification with Local Binary Patterns. IEEE
Trans on Pattern Analysis And Machine Intelligence, 2002.
 Qing Jun Wang and Ru Bo Zhang. LPP-HOG: A New Local Image
Descriptor for Fast Human Detection. In KAM, 2008.
 Hui-Xing Jia, Yu-Jin Zhang. Fast Human Detection by Boosting
Histograms of Oriented Gradients. In ICIG, 2007.
 Stefan Hinterstoisser, Vincent Lepetit, Slobodan Ilic, Pascal Fua and
Nassir Navab. Dominant Orientation Templates for Real-Time Detection
of Texture-Less Objects. In CVPR, 2009.
 Simon Taylor and Tom Drummond. Multiple Target Localisation at over
100 FPS. In BMVC, 2009.
 M.Ozuysal, P.Fua, and V.Lepetit. Fast key point recognition in ten lines
of code. In CVPR, 2007.
 P. Viola and M. Jones. Robust real-time object detection. IJCV, 2001.