Adaptive deep metric embeddings for person re-identification under occlusions

Abstract

Person re-identification (ReID) under occlusions is a challenging problem in video surveillance. Most of existing person ReID methods take advantage of local features to deal with occlusions. However, these methods usually independently extract features from the local regions of an image without considering the relationship among different local regions. In this paper, we propose a novel person ReID method, which learns the spatial dependencies between the local regions and extracts the discriminative feature representation of the pedestrian image based on Long Short-Term Memory (LSTM), dealing with the problem of occlusions. In particular, we propose a novel loss (termed the adaptive nearest neighbor loss) based on the classification uncertainty to effectively reduce intra-class variations while enlarging inter-class differences within the adaptive neighborhood of the sample. The proposed loss enables the deep neural network to adaptively learn discriminative metric embeddings, which significantly improve the generalization capability of recognizing unseen person identities. Extensive comparative evaluations on challenging person ReID datasets demonstrate the significantly improved performance of the proposed method compared with several state-of-the-art methods.

View-aware attribute-guided network for vehicle re-identification

“…Yan et al [39] investigated the multi-grain associations between vehicles with different attributes. Yang et al [40] dealt with occlusion problem using long short term memory network. Liu et al [28] proposed a multi-branch network to extract features like instance differences, and model information.…”

Feature-Level Camera Style Transfer for Person Re-Identification

“…These quantitative results show that the proposed CST approach is suitable for both global and partial features; thus, it can be applied in multiple person re-ID baselines, and it outperforms state-of-the-art methods. LGMANet [33] 82.7 94.0 --MTNet [34] 81.5 93.9 --RIN [35] 67.6 86.1 --RNLSTM [36] 76.9 90.6 --Gconv [37] 72.3 88.1 95.1 96.8 Top-DB-Net [38] 85.8 94.9 --DTr [39] 76.7 91.2 --MSFANet [40] 82.3 92.9 --DeformGAN [41] 61.3 80.6 --LRSO [42] 66.1 84.0 --CamStyle [14] 71.6 89.5 --FD-GAN [43] 77.7 90.5 --DRANet [44] 79 Furthermore, some qualitative comparisons are presented to vividly show the improvement brought by the proposed CST approach. Figure 8 shows the retrieved results of two example query images in the Market-1501 dataset.…”

Reliable Fault Diagnosis of Rolling Bearing Based on Ensemble Modified Deep Metric Learning

“…e network architecture of DML consists of input layer, multiple hidden layers, and output layer, which can be seen in Figure 2. It can also compute the feature representation h (N) of a data sample x by passing it to multiple-layer nonlinear transformations and map the original feature parameters to discriminative feature space by maximizing interclass variation and minimizing intraclass variation [15,16].…”

“…Deep metric learning (DML) which can map original feature parameters to discriminative feature space by maximizing interclass variation and minimizing intraclass variation is also suggested to be applied to the field of pattern recognition [15][16][17][18]. ese DML models can use the distance metric criterion to classify the data samples with explainable classification mechanism, but in the field of fault diagnosis some mechanical signals are too difficult to be diagnosed because of the complexity of signal transmission path and insensitiveness of the fault parameter features to fault categories; in particular, some data samples in the boundary region of different fault categories can be misclassified by the DML based on distance metric criterion [18].…”