Pedestrian re-identification tasks lack the consideration of the pedestrian feature scale variation during feature extraction, so that they are easily affected by environment and have low accuracy of pedestrian re-identification. In order to solve the problem, a pedestrian re-identification method based on multi-scale feature fusion was proposed. Firstly, in the shallow layer of the network, multi-scale pedestrian features were extracted through mixed pooling operation, which was helpful to improve the feature extraction capability of the network. Then, strip pooling operation was added to the residual block to extract the remote context information in horizontal and vertical directions respectively, which avoided the interference of irrelevant regions. Finally, after the residual network, the dilated convolutions with different scales were used to further preserve the multi-scale features, so as to help the model to analyze the scene structure flexibly and effectively. Experimental results show that, on Market-1501 dataset, the proposed method has the Rank1 of 95.9%, and the mean Average Precision (mAP) of 88.5%; on DukeMTMC-reID dataset, the proposed method has the Rank1 of 90.1%, and the mAP of 80.3%. It can be seen that the proposed method can retain the pedestrian feature information better, thereby improving the accuracy of pedestrian re-identification tasks.

In order to solving the problem that the pedestrian detection algorithm based on Aggregated Channel Feature (ACF) has a low detection precision and a high false detection rate in complex scenes, a multi-channel pedestrian detection algorithm combined with features of texture and contour was proposed in this paper. The algorithm flows included training classifier and detection. In the training phase, the ACF, the texture features of Local Binary Patterns (LBP) and the contour features of Sketch Tokens (ST) were extracted, and trained separately by the Real AdaBoost classifier. In the detection phase, the cascading detection idea was used. The ACF classifier was used to deal with all objects, then the complicated classifier of LBP and ST were used to gradually filter the result of the previous step. In the experiment, the INRIA data set was used in the simulation of our algorithm, the results show that our algorithm achieves a Log-Average Miss Rate (LAMR) of 13.32%. Compared with ACF algorithm, LAMR is decreased by 3.73 percent points. The experimental results verify that LBP and ST can be used as a complementation of ACF. So some objects of false detection can be eliminated in the complicated scenes and the accuracy can be improved. At the same time, the efficiency of multi-feature algorithm is ensured by cascading detection.

Aiming at false segmentation of small regions and high computational complexity in traditional color image segmentation algorithm, a hierarchical method of color image segmentation based on rough set and HIS (Hue-Saturation-Intensity) space was proposed. Firstly, for the reason that the singularities in HSI space are the achromatic pixels in RGB space, the achromatic regions of RGB space were segmented and labeled in order to remove the singularities from the original image. Secondly, the original image was converted from RGB space to HSI space. In intensity component, in view of spatial neighbor information and regional distribution difference, the original histogram was weighted by homogeneity function with changing thresholds and gradience. The weighted and original histograms were respectively used as the upper and lower approximation sets of rough set. The new roughness function was defined and applied to image segmentation. Then the different regions obtained in the previous stage were segmented according to the histogram in hue component. Finally, the homogeneous regions were merged in RGB space in order to avoid over-segmentation. Compared with the method based on rough set proposed by Mushrif etc. (MUSHRIF M M, RAY A K. Color image segmentation: rough-set theoretic approach. Pattern Recognition Letters, 2008, 29(4): 483-493), the proposed method can segment small regions easily, avoid the false segmentation caused by the correlation between RGB color components, and the executing speed is 5-8 times faster. The experimental results show the proposed method yields better segmentation, and it is efficient and robust to noise.