一种改进聚合通道特征的行人检测方法

行人检测是计算机视觉和模式识别领域的研究热点与难点，针对聚合通道特征（Aggregate channel feature，ACF）算法应用于行人检测时，出现检测精度较低、平均对数漏检率（Log-average miss rate，LAMR）较高的情况，提出一种改进的ACF行人检测算法。首先结合objectness方法对ACF算法低得分区域进行进一步验证，可以在一定程度上减少算法的误检数；其次结合检测窗口的得分及位置信息，对非极大值抑制算法（Non-maximum suppression，Nms）进行改进，平均精度（Average precision，AP）提升了0.41%，LAMR降低了1.49%；最后采用星型可形变部件模型（Star-cascade DPM，casDPM）对一定阈值下的得分检测窗口进行级联检测，AP提升了0.65%，LAMR降低了2.06%。在INRIA数据集上实验表明，满足实时检测的条件下，极大地降低了误检数，具有较好的行人检测效果。

Improved Pedestrian Detection Method of Modified Aggregate Channel Feature

Pedestrian detection is a highspot and challenge research work in the area of computer vision and pattern recognition. The aggregate channel feature (ACF) algorithm generates lower detecting precision and higher log-average miss rate(LAMR) for pedestrian detection. We proposed an improve pedestrian detection method based on ACF algorithm in this paper. Firstly, we introduce objectness method to further verify low detection score object area captured by ACF, which can reduce false positive (FP) of the algorithm to some degree. Then, we combine the score with location of the detection window to modify the non-maximum suppression (Nms) algorithm, and the AP increases by 0.41%, while the LAMR decreases by 1.49%. Finally, we implement cascading detection for detection area by using a given threshold score and a casDPM model. The AP increases by 0.65%, and the LAMR decreases by 2.06%. Experiments on INRIA dataset are conducted and validated, and the results show that our approach not only meets the needs of real-time detection, but also obviously decreases FP, and displays a good detection effect.