基于梯度双面互补特性的级联快速目标检测

针对目标检测中精度和速度难以兼顾的问题，借助视觉注意理论中的目标感知与识别机制，分析目标描述中梯度幅值与梯度方向信息之间具有的互补性，提出了基于两层级联梯度特征的快速目标检测模型，可有效描述类无关和类相关检测器.一方面，采用梯度幅值特征，从滑动窗口采样中获得候选目标提议，大幅降低了验证窗口的数量，确保检测速度，另一方面，利用级联方式学习训练多个子检测器，可更好实现不同尺度变化下的目标检测精度.PASCAL数据集上的实验结果，解释了级联梯度特征对目标结构描述的有效性，表明了该文方法在与现有先进方法的检测精度相当的前提下，可极大提升检测速度.

The Cascaded Rapid Object Detection with Double-Sided Complementary in Gradients

To address the dilemma of trade-off between efficiency and accuracy for object detection,based on the mechanism of object perception and recognition in visual attention theory,the two sides derived from gradient feature as magnitude and direction have been revisited to manifest their complementary characteristics.The new rapid object detection model based on two-layer cascade with gradients is motivated,making two types of category-independent and category-dependent detectors efficiently described.On the one hand,gradient magnitude can be used to generate the efficient object proposal in clutter from sliding window samples which guarantees the significant decrease on the number of windows for candidate and speeds up detection.On the other hand,the cascade-architecture in form of multiple sub-detectors can well adapt to the varying scales of different objects resulting in boost of accuracy.Experimental performance in PASCAL presents the effectiveness of cascade structure for gradient features,and demonstrates that our model can dramatically speed up the detection with the advantages of comparable accuracy against the state-of-the-art.