A New Object Detection Algorithm Using Local Contour Features
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摘要: 针对复杂场景中背景复杂、目标周围噪声多及目标只占图像中较小部分而难于检测的问题,提出一种新的基于局部轮廓特征的检测目标方法.该方法首先利用改进的全局概率边界算法 (Globalized probability of boundary, gPb) 算法提取图像的轮廓,然后应用最大类间方差法 (Otsu)进行自动阈值处理得到图像的显著性轮廓; 再提取显著性轮廓的k邻近大致直线轮廓段(k connected roughly straight contour segments, kAS),并以kAS作为局部特征,用于复杂场景中的目标检测.该算法结合 gPb 算法和 Otsu 提取轮廓的显著性轮廓,去除了目标附近的大量噪声边界,有效地提高了检测效率.同时,在检测阶段,测试集与 训练集中提取的不相关特征数目也得到较大减少,从而提高了检测的精度.多组实验结果均表明本文方法的有效性.Abstract: It is difficult to detect objects in complex scene in which more noise is around the object or the object is only a small portion of the image. In order to solve the problem, a new object detection algorithm based on local contour features is proposed in this paper. Firstly, an improved gPb (globalized probability of boundary) algorithm is used to extract the outline of the image. Then the Otsu for automatic threshold processing is applied to obtain the significant contour. Next, k connected roughly straight contour segments (k adjacent segments, kAS) are extracted and used as a local feature for object detection in complex scenes. The algorithm combines gPb algorithm and Otsu to extract significant contour, thus it can remove much noise around the object boundary, and effectively improve the detection efficiency as well. Meanwhile, in the detection phase, the numbers of irrelevant features in the test set and the training set are largely reduced, therefore the detection accuracy is improved. Multiple sets of experimental results demonstrate the effectiveness of this method.
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Key words:
- Contour extraction /
- local contour features /
- threshold processing /
- object detection
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[1] He Chu, Yin Sha, Xu Lian-Yu, Liao Zi-Xian. Feature extraction of SAR image based on local important sampling binary encoding. Acta Automatica Sinica, 2014, 40(2): 316-326 (何楚, 尹莎, 许连玉, 廖紫纤. 基于局部重要性采样的SAR图像纹理特征提取方法. 自动化学报, 2014, 40(2): 316-326) [2] Chong Yan-Wen, Kuang Hu-Lin, Li Qing-Quan. Two-stage pedestrian detection based on multiple features and machine learning. Acta Automatica Sinica, 2012, 38(3): 375-381 (种衍文, 匡湖林, 李清泉. 一种基于多特征和机器学习的分级行人检测方法. 自动化学报, 2012, 38(3): 375-381) [3] [3] Ren X F, Ramanan D. Histograms of sparse codes for object detection. Computer Vision and Pattern Recognition (CVPR), 2013: 3246-3253 [4] Zhu Hai-Long, Liu Peng, Liu Jia-Feng, Tang Xiang-Long. A graph analysis method for abnormal crowd state detection. Acta Automatica Sinica, 2012, 38(5): 742-750 (朱海龙, 刘鹏, 刘家锋, 唐降龙. 人群异常状态检测的图分析方法. 自动化学报, 2012, 38(5): 742-750) [5] [5] Lazebnik S, Schmid C, Ponce J. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories. In: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2006, 2: 2169-2178 [6] [6] Shotton J, Blake A, Cipolla R. Contour-based learning for object detection. In: Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing: IEEE, 2005, 1: 503-510 [7] [7] Ferrari V, Fevrier L, Jurie F, Schmid C. Groups of adjacent contour segments for object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(1): 36-51 [8] [8] Toshev A, Taskar B, Daniilidis K. Shape-based object detection via boundary structure segmentation. International Journal of Computer Vision, 2012, 99(2): 123-146 [9] [9] Ferrari V, Jurie F, Schmid C. From images to shape models for object detection. International Journal of Computer Vision, 2010, 87(3): 284-303 [10] Arbelaez P, Maire M, Fowlkes C, Malik J. Contour detection and hierarchical image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(5): 898-916 [11] Otsu N. A threshold selection method from gray-level histograms. Automatica, 1975, 11(285-296): 23-27 [12] Ferrari V, Tuytelaars T, Van Gool L. Object detection by contour segment networks. Computer Vision - ECCV 2006. Berlin Heidelberg: Springer, 2006. 14-28 [13] Fu Zhong-Liang. Some new methods for image threshold selection. Computer Applications, 2000, 20(10): 13-15 (付忠良. 一些新的图像阈值选取方法. 计算机应用, 2000, 20(10): 13-15) [14] Csurka G, Dance C R, Fan L X, Willamowski J, Bray C. Visual categorization with bags of keypoints. In: Proceedings of the 2004 Workshop on Statistical Learning in Computer Vision, ECCV. 2004, 1-22 [15] Ferrari V, Tuytelaars T, Van Gool L. Real-time affine region tracking and coplanar grouping. Computer Vision and Pattern Recognition, 2001, 2(2): 226-233 [16] Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2005, 1: 886-893 [17] Ying Wen-Hao, Wang Shi-Tong, Deng Zhao-Hong, Wang Jun. Support vector machine for domain adaptation based on class distributiond. Acta Automatica Sinica, 2013, 39(8): 1273-1288 (应文豪, 王士同, 邓赵红, 王骏. 基于类分布的领域自适应支持向量机. 自动化学报, 2013, 39(8): 1273-1288) [18] Porikli F. Integral histogram: A fast way to extract histograms in cartesian spaces. In: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE, 2005, 1: 829- 836 [19] Mikolajczyk K, Schmid C. Indexing based on scale invariant interest points. In: Proceedings of the 8th IEEE International Conference on Computer Vision. Vancouver, BC: IEEE. 2001, 1: 525-531 [20] Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
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