Enhancing the discriminative feature learning for visible-thermal cross-modality person re-identification

Abstract

Existing person re-identification has achieved great progress in the visible domain, capturing all the person images with visible cameras. However, in a 24-hour intelligent surveillance system, the visible cameras may be noneffective at night. In this situation, thermal cameras are the best supplemental components, which capture images without depending on visible light. Therefore, in this paper, we investigate the visible-thermal cross-modality person re-identification (VT Re-ID) problem. In VT Re-ID, there are two knotty problems should be well handled, cross-modality discrepancy and intra-modality variations. To address these two issues, we propose focusing on enhancing the discriminative feature learning (EDFL) with two extreme simple means from two core aspects, (1) skip-connection for mid-level features incorporation to improve the person features with more discriminability and robustness, and (2) dual-modality triplet loss to guide the training procedures by simultaneously considering the cross-modality discrepancy and intra-modality variations. Additionally, the two-stream CNN structure is adopted to learn the multi-modality sharable person features. The experimental results on two datasets show that our proposed EDFL approach distinctly outperforms state-of-the-art methods by large margins, demonstrating the effectiveness of our EDFL to enhance the discriminative feature learning for VT Re-ID.

MSO: Multi-Feature Space Joint Optimization Network for RGB-Infrared Person Re-Identification

“…In this subsection, we compare our proposed method with several cross-modality person ReID methods that include the following categories: 1) With different structures and loss functions, Two-Stream, One-Stream, Zero-Padding [39], HSME, D-HSME [11], BDTR, SDL [19], DGD+MSR [7], EDFL [23], HPILN [50], AGW [44], cm-SSFT [25], and TSLFN+HC [54] learned modality-invariant feature representation; 2) With the ideas of GAN, cmGAN [4],…”

MFCNet: Mining Features Context Network for RGB–IR Person Re-Identification

“…Previous works [12,44] provided results for most of the considered methods. The methods compared in this paper include manual feature design methods (HOG [45] and LOMO [3]) and deep learning methods [9,15,[24][25][26][27]29,[33][34][35][46][47][48][49][50]. The results of the compared methods were obtained from the original text.…”

“…In this approach, the mode is irrelevant. Liu et al [26] use two-way convolution to extract features. In the process of extracting features, skip connections are used to fuse the middle layers of the CNN model and enhance the robustness and non-descriptiveness of the extracted features.…”

“…Due to the differences among the modalities of input images, this paper uses an infrared and visible-light dual-path network as the basic structure. Based on [15,25,26], the backbone network for feature extraction in this paper is Resnet50 [38], and SGA modules are embedded between the second layer and the third layer and the third layer and the fourth layer. The SGA module designed in this paper can extract the features of key parts of images and mine the relationships among contextual features.…”

“…In this paper, weighted regularized triple loss function L wrt and identity loss function L id are combined for network training to learn discriminative features [12,15,[24][25][26][27][28][29]. Therefore, the loss function is defined as:…”