An enhanced deep feature representation for person re-identification

Wu, Shangxuan; Chen, Ying-Cong; Li, Xiang; Wu, Ancong; You, Jinjie; Zheng, Wei‐Shi

doi:10.1109/wacv.2016.7477681

Cited by 210 publications

(152 citation statements)

References 33 publications

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“…11. Wu et al [44] propose Feature Fusion Net (FFN) to describe human appearance for human re-id, where the FFN combines convolutional neural network (CNN) deep feature with handcrafted features, including color histogram computed in five different color spaces, i.e., RGB, HSV, YCbCr, Lab and YIQ, and Gabor texture descriptors with multi-scale and multi-orientation. The CNN deep feature is constrained by the handcrafted features through backpropagation to form a more discriminative feature fusion deep neural network.…”

Section: Map Optimization Solution Frameworkmentioning

confidence: 99%

Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across nonoverlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

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Section: Map Optimization Solution Frameworkmentioning

confidence: 99%

Human tracking over camera networks: a review

Hou

Wan

Hwang

et al. 2017

EURASIP J. Adv. Signal Process.

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“…With the development of deep neural network, there are also some work using deep neural networks [8,12]. In the work, the Feature Fusion Network (FFN) [8] combines handcrafted features and deep neural features together, which gives us inspiration.…”

Section: Feature Fusion Networkmentioning

confidence: 99%

Multi-View Feature Fusion Network for Vehicle Re-Identification

Wu¹,

Liu²,

Zhou³

et al. 2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…In [62,63], low-level hand-crafted features are combined with high-level CNN features. Afterwards, metric learning is applied on the obtained combination.…”

Section: B Deep Cnn Learningmentioning

confidence: 99%

“…Afterwards, metric learning is applied on the obtained combination. In [62], the CNN model is first learned by adding a fusion layer that combines CNN features with the hand-crafted low-level ELF [64] features. Afterwards, the high-level resulting feature is concatenated with LOMO, and subsequently presented to the KMFA [65] metric learning, which considerably boosts the re-ID accuracy.…”

Section: B Deep Cnn Learningmentioning

confidence: 99%

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

Mejdoub¹,

Ksibi²,

Amar³

et al. 2017

ijacsa

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Abstract-Person re-identification (re-ID) is a challenging task in the camera surveillance field, since it addresses the problem of re-identifying people across multiple non-overlapping cameras. Most of existing approaches have been concentrated on: 1) achieving a robust and effective feature representation; and 2) enforcing discriminative metric learning to predict if two images represent the same identity. In this context, we present a new approach for person re-ID built upon multi-level descriptors. This is achieved by combining three complementary representations: salient-Gaussian Fisher Vector (SGFV) encoding method, salient-Gaussian BossaNova (SGBN) histogram encoding method and deep Convolutional Neural Network (CNN) features. The two first methods adapt the histogram encoding framework to the person re-ID task. This is achieved by integrating the pedestrian saliency map and the spatial location information, in the histogram encoding process. On one hand, human saliency is reliable and distinctive in the person re-ID task, since it can model the uniqueness of the identity. On the other hand, localizing a person in the image can effectively discard noisy background information. Finally, one of the most advanced metric learning in person re-ID: the Cross-view Quadratic Discriminant Analysis (XQDA) is applied on the top of the resulting description. The proposed method yields promising person re-ID results on two challenging image-based person re-ID benchmarks: CUHK03 and Market-1501.

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An enhanced deep feature representation for person re-identification

Cited by 210 publications

References 33 publications

Human tracking over camera networks: a review

Human tracking over camera networks: a review

Multi-View Feature Fusion Network for Vehicle Re-Identification

Person re-ID while Crossing Different Cameras: Combination of Salient-Gaussian Weighted BossaNova and Fisher Vector Encodings

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