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Efficient Rotation Invariant Object Detection using Boosted Random Ferns Abstract - We present a new approach for building an efficient and robust classifier for the two class problem, that localizes objects that may appear in the image under different orientations. In contrast to other works that adddress this problem using multiple classifiers, each one specialized for a specific orientation, or using multi-class classifiers, we propose a more straightforward approach that consists of two simple stages, namely estimation and classification. The estimator yields an initial object orientation hypothesis, which is then used by the classifier. False positive orientations provided by the estimator are rejected during the classification stage. This methodology allows reducing the time complexity of the algorithm while classification results remain high. The classifier we use in both stages is based on a boosted combination of Random Ferns over local histograms of oriented gradients (HOGs) in order to capture local statistics from gradient-based features. These are also another remarkable differences with respect to current methods where Random Ferns are computed over image intensity domain and without a supervised learning. We evaluate our method on standard databases, and show that it yields competitive results comparable to state-of-the-art approaches while being significantly more efficient. Furthermore, a new database for object detection under in-plane rotations is presented. This dataset contains motorbike instances with challenging conditions such as cluttered background, different illumination conditions and partial occlusions. |
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