Feedback mechanism based iterative metric learning for person re-identification

Ren, Yutao; Li, Xuelong; Lu, Xiaoqiang

doi:10.1016/j.patcog.2017.04.012

Cited by 8 publications

(3 citation statements)

References 15 publications

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“…Due to the poor quality of cameras or the extent distance from the person, the captured pedestrian videos usually suffer from low resolution, which results in the loss of useful information contained in videos. In [21], they introduce a mean distance of multi-metric subspace to address the overfitting problem, usually presented in learned metric subspace-based methods.…”

Section: Related Workmentioning

confidence: 99%

“…3) Descriptor design. Data obtained by detection and tracking modules are used to segment the person's image for generating the descriptor, which can get constructed from data/cues as face [9]- [13]; visual appearance of the whole body [8], [14]- [21]; walking pattern [22], [23]; height and build [1]; and head, torso and limbs of a person [24], [25]; or a combination of these cues. 4) Descriptors matching.…”

Section: Introductionmentioning

confidence: 99%

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Target Field of View Prediction Using Artificial Pheromones for People Reidentification

et al. 2019

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People reidentification is a fundamental task in automated video surveillance based on computer vision. Reidentification happens when a person seen in a field of view is the same that has been observed in other fields of view. A person who has disappeared from one field of view can appear in any other within a camera network. Instead of looking for the person in all neighboring fields of view, for an intelligent video surveillance system, it is more practical to predict which of the neighboring camera views the person could appear. This prediction can become achieved by learning the paths the person usually follows in the camera network. The ant colony optimization technique has properties that can get exploited for this purpose; precisely, the accumulation and evaporation of artificial pheromones are used to learn the paths. After the learning process, the proposed method can make predictions every time that the person leaves a field of view. Such prediction is evaluated to obtain feedback and further tune the learning process. The path followed by the person becomes obtained by tracking their face image within and between fields of view using correlation filters as descriptors. The results obtained from an extensive experiment show that the field of view that the person selects to visit can be successfully predicted using artificial pheromones, and thus, reduce the resources that require reidentification.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Target Field of View Prediction Using Artificial Pheromones for People Reidentification

et al. 2019

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“…1b, the 'inter-class' variation of different persons may be much smaller as compared with the 'intra-class' variation of the same person. Most traditional methods address these challenges by either designing discriminative features [1,2,3,4,5,6,7,8] or learning powerful similarity metrics [9,10,11,12,7,13,14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

Attention driven person re-identification

Yang

Yan

et al. 2019

Pattern Recognition

170

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Person re-identification (ReID) is a challenging task due to arbitrary human pose variations, background clutters, etc. It has been studied extensively in recent years, but the multifarious local and global features are still not fully exploited by either ignoring the interplay between whole-body images and body-part images or missing in-depth examination of specific body-part images. In this paper, we propose a novel attention-driven multi-branch network that learns robust and discriminative human representation from global whole-body images and local body-part images simultaneously. Within each branch, an intra-attention network is designed to search for informative and discriminative regions within the whole-body or body-part images, where attention is elegantly decomposed into spatial-wise attention and channel-wise attention for effective and efficient learning. In addition, a novel inter-attention module is designed which fuses the output of intra-attention networks adaptively for optimal person ReID. The proposed technique has been evaluated over three widely used datasets CUHK03, Market-1501 and DukeMTMC-ReID, and experiments demonstrate its superior robustness and effectiveness as compared with the state of the arts.

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