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Register a new userUnity Style Transfer for Person Re-Identification
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Added by
Chong Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Style variation has been a major challenge for person re-identification, which aims to match the same pedestrians across different cameras. Existing works attempted to address this problem with camera-invariant descriptor subspace learning. However, there will be more image artifacts when the difference between the images taken by different cameras is larger. To solve this problem, we propose a UnityStyle adaption method, which can smooth the style disparities within the same camera and across different cameras. Specifically, we firstly create UnityGAN to learn the style changes between cameras, producing shape-stable style-unity images for each camera, which is called UnityStyle images. Meanwhile, we use UnityStyle images to eliminate style differences between different images, which makes a better match between query and gallery. Then, we apply the proposed method to Re-ID models, expecting to obtain more style-robust depth features for querying. We conduct extensive experiments on widely used benchmark datasets to evaluate the performance of the proposed framework, the results of which confirm the superiority of the proposed model.
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Person Re-Identification (re-id) is a challenging task in computer vision, especially when there are limited training data from multiple camera views. In this paper, we pro- pose a deep learning based person re-identification method by transferring knowledge of mid-level attribute features and high-level classification features. Building on the idea that identity classification, attribute recognition and re- identification share the same mid-level semantic representations, they can be trained sequentially by fine-tuning one based on another. In our framework, we train identity classification and attribute recognition tasks from deep Convolutional Neural Network (dCNN) to learn person information. The information can be transferred to the person re-id task and improves its accuracy by a large margin. Further- more, a Long Short Term Memory(LSTM) based Recurrent Neural Network (RNN) component is extended by a spacial gate. This component is used in the re-id model to pay attention to certain spacial parts in each recurrent unit. Experimental results show that our method achieves 78.3% of rank-1 recognition accuracy on the CUHK03 benchmark.
Recent years have witnessed a substantial increase in the deep learning (DL)architectures proposed for visual recognition tasks like person re-identification,where individuals must be recognized over multiple distributed cameras. Althoughthese architectures have greatly improved the state-of-the-art accuracy, thecomputational complexity of the CNNs commonly used for feature extractionremains an issue, hindering their deployment on platforms with limited resources,or in applications with real-time constraints. There is an obvious advantage toaccelerating and compressing DL models without significantly decreasing theiraccuracy. However, the source (pruning) domain differs from operational (target)domains, and the domain shift between image data captured with differentnon-overlapping camera viewpoints leads to lower recognition accuracy. In thispaper, we investigate the prunability of these architectures under different designscenarios. This paper first revisits pruning techniques that are suitable forreducing the computational complexity of deep CNN networks applied to personre-identification. Then, these techniques are analysed according to their pruningcriteria and strategy, and according to different scenarios for exploiting pruningmethods to fine-tuning networks to target domains. Experimental resultsobtained using DL models with ResNet feature extractors, and multiplebenchmarks re-identification datasets, indicate that pruning can considerablyreduce network complexity while maintaining a high level of accuracy. Inscenarios where pruning is performed with large pre-training or fine-tuningdatasets, the number of FLOPS required by ResNet architectures is reduced byhalf, while maintaining a comparable rank-1 accuracy (within 1% of the originalmodel). Pruning while training a larger CNNs can also provide a significantlybetter performance than fine-tuning smaller ones.
In real-world video surveillance applications, person re-identification (ReID) suffers from the effects of occlusions and detection errors. Despite recent advances, occlusions continue to corrupt the features extracted by state-of-art CNN backbones, and thereby deteriorate the accuracy of ReID systems. To address this issue, methods in the literature use an additional costly process such as pose estimation, where pose maps provide supervision to exclude occluded regions. In contrast, we introduce a novel Holistic Guidance (HG) method that relies only on person identity labels, and on the distribution of pairwise matching distances of datasets to alleviate the problem of occlusion, without requiring additional supervision. Hence, our proposed student-teacher framework is trained to address the occlusion problem by matching the distributions of between- and within-class distances (DCDs) of occluded samples with that of holistic (non-occluded) samples, thereby using the latter as a soft labeled reference to learn well separated DCDs. This approach is supported by our empirical study where the distribution of between- and within-class distances between images have more overlap in occluded than holistic datasets. In particular, features extracted from both datasets are jointly learned using the student model to produce an attention map that allows separating visible regions from occluded ones. In addition to this, a joint generative-discriminative backbone is trained with a denoising autoencoder, allowing the system to self-recover from occlusions. Extensive experiments on several challenging public datasets indicate that the proposed approach can outperform state-of-the-art methods on both occluded and holistic datasets
Person re-identification (ReID) aims to match people across multiple non-overlapping video cameras deployed at different locations. To address this challenging problem, many metric learning approaches have been proposed, among which triplet loss is one of the state-of-the-arts. In this work, we explore the margin between positive and negative pairs of triplets and prove that large margin is beneficial. In particular, we propose a novel multi-stage training strategy which learns incremental triplet margin and improves triplet loss effectively. Multiple levels of feature maps are exploited to make the learned features more discriminative. Besides, we introduce global hard identity searching method to sample hard identities when generating a training batch. Extensive experiments on Market-1501, CUHK03, and DukeMTMCreID show that our approach yields a performance boost and outperforms most existing state-of-the-art methods.
Video-based person re-identification has drawn massive attention in recent years due to its extensive applications in video surveillance. While deep learning-based methods have led to significant progress, these methods are limited by ineffectively using complementary information, which is blamed on necessary data augmentation in the training process. Data augmentation has been widely used to mitigate the over-fitting trap and improve the ability of network representation. However, the previous methods adopt image-based data augmentation scheme to individually process the input frames, which corrupts the complementary information between consecutive frames and causes performance degradation. Extensive experiments on three benchmark datasets demonstrate that our framework outperforms the most recent state-of-the-art methods. We also perform cross-dataset validation to prove the generality of our method.
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