Deep Ranking for Person Re-Identification via Joint Representation Learning

This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems. Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metri...

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Vydané v:IEEE transactions on image processing Ročník 25; číslo 5; s. 2353 - 2367
Hlavní autori: Chen, Shi-Zhe, Guo, Chun-Chao, Lai, Jian-Huang
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.05.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithm design and analysis
Algorithms
Biometric Identification - methods
Cameras
Databases, Factual
deep convolutional neural network
Feature extraction
Galleries
Humans
Image color analysis
Learning
learning to rank
Machine Learning
Measurement
Neural networks
Neural Networks (Computer)
Person re-identification
Probes
Ranking
Representations
Similarity
deep convolutional neural network
Person re-identification
learning to rank
ISSN:1057-7149, 1941-0042, 1941-0042
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Shrnutí:This paper proposes a novel approach to person re-identification, a fundamental task in distributed multi-camera surveillance systems. Although a variety of powerful algorithms have been presented in the past few years, most of them usually focus on designing hand-crafted features and learning metrics either individually or sequentially. Different from previous works, we formulate a unified deep ranking framework that jointly tackles both of these key components to maximize their strengths. We start from the principle that the correct match of the probe image should be positioned in the top rank within the whole gallery set. An effective learning-to-rank algorithm is proposed to minimize the cost corresponding to the ranking disorders of the gallery. The ranking model is solved with a deep convolutional neural network (CNN) that builds the relation between input image pairs and their similarity scores through joint representation learning directly from raw image pixels. The proposed framework allows us to get rid of feature engineering and does not rely on any assumption. An extensive comparative evaluation is given, demonstrating that our approach significantly outperforms all the state-of-the-art approaches, including both traditional and CNN-based methods on the challenging VIPeR, CUHK-01, and CAVIAR4REID datasets. In addition, our approach has better ability to generalize across datasets without fine-tuning.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2016.2545929