使用单幅图像进行特定目标的检测是机器视觉领域的重要任务之一。利用机器学习的方法, 使用LSVM分类器进行人形目标的检测。该方法提取图像的HOG(梯度方向直方图)特征和其对应的可变形部件来描述目标的外形特征, 能够较好地解决目标由于运动而产生外形变化的问题。对常见公共区域场景进行数据采集并随机抽取了200张图像, 使用所述方法对其中共1 100个人形目标进行检测, 正确率识别率为78.3%。结果表明该方法具有一定的可行性和稳定性, 能够较好检测出单幅图像中的人形目标并加以标注。但对于某种程度有所遮蔽的人形目标则会产生漏检的现象。

It is one of the important tasks in machine vision field to detect specific object using single image. A machine learning approach using latent support vector machine(LSVM) classifier was presented for detecting human-kind shape object. The histogram of oriented gradients(HOG) features were extracted and the corresponding deformable parts were formed to describe the appearance of the object. The problem that the object’s appearance deformed when it was in motion, was solved. 200 images captured in typical public areas were randomly selected and used to perform this method. 1 100 human-kind shape objects from them were tested, and the correct recognition rate of 78.3% was achieved. The experimental results show that this approach is able to detect human-kind shapes and mark them out which demonstrate its feasibility and stability;however,misdetection can happen when the human-kind shape object is partly covered.