基于特征融合的行人重识别算法

Research on the person re-identification algorithm based on feature fusion

为解决实际行人重识别系统中识别率低、识别速度慢的问题,从创新和工程应用出发,提出了一种行人重识别算法。对行人图片进行预处理,采用色调、饱和度、亮度(hue,saturation,value,HSV)空间非线性量化的方法构建颜色命名空间,对人体分区域预识别来提高检测效率;对备选目标的整幅图像提取HSV和方向梯度直方图(histogram of oriented gradient,HOG)作为整体特征并在滑动窗口内提取颜色命名(color naming,CN)特征和2个尺度的尺度不变特征(scale invariant local pattern,SILTP),采用本文融合算法得到新的特征;在3个数据集上进行行人重识别,融合的特征在2种度量学习算法的Rank1平均提高了2.4%和3.3%。实验结果表明该算法能够提高重识别精度。

Starting from the innovation and engineering application,a new pedestrian re-identification algorithm was proposed,which mainly solved the problem of low recognition rate and slow recognition speed in the actual pedestrian recognition system.Preprocessing the pedestrian image,using the hue,saturation,and value(HSV)spatial nonlinear quantization method to construct the color namespace,pre-identifying the human sub-regions to improve the recognition speed;extracting the HSV and direction gradient histogram of the entire oriented gradient(HOG)features as the overall feature;and sliding on the entire image of the candidate target,the color naming(CN)features and the scale-invariant local pattern(SILTP)features of the two scales are extracted in the window,getting new features by new fusion algorithms.Pedestrian re-identification is carried out on three data sets.The fusion features improve the average Rank1s of two metric learning algorithms by 2.4%and 3.3%on average.Experimental results show that the algorithm can improve the accuracy of re-identification.