Consistent-Aware Deep Learning for Person Re-identification in a Camera Network

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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A Strong and Efficient Baseline for Vehicle Re-Identification Using Deep Triplet Embedding

Journal of Artificial Intelligence and Soft Computing Research ◽

10.2478/jaiscr-2020-0003 ◽

2020 ◽

Vol 10 (1) ◽

pp. 27-45 ◽

Cited By ~ 5

Author(s):

Ratnesh Kumar ◽

Edwin Weill ◽

Farzin Aghdasi ◽

Parthasarathy Sriram

Keyword(s):

Deep Learning ◽

Smart City ◽

State Of The Art ◽

Loss Functions ◽

Camera Network ◽

Formal Evaluation ◽

Cross Domain ◽

Extensive Evaluation ◽

Data Points ◽

Triplet Loss

AbstractIn this paper we tackle the problem of vehicle re-identification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for re-identification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of triplet loss applied to vehicle re-identification and demonstrate that using the recently proposed sampling approaches for mining informative data points outperform most of the existing state-of-the-art approaches for vehicle re-identification. Compared to most existing state-of-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature. In addition to the conference version [24], this submission adds extensive experiments on new released datasets, cross domain evaluations and ablation studies.

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Pedestrian Tracking Combined with Deep Learning and Camera Network Topology in Non-overlapping Multi-camera Surveillance

2019 IEEE 14th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) ◽

10.1109/iske47853.2019.9170386 ◽

2019 ◽

Author(s):

Mianjin Wei ◽

Jihong Pei

Keyword(s):

Deep Learning ◽

Network Topology ◽

Camera Network ◽

Pedestrian Tracking ◽

Camera Surveillance

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A unified framework for automated person re-identification

Transport and Communication Science Journal ◽

10.47869/tcsj.71.7.11 ◽

2020 ◽

Vol 71 (7) ◽

pp. 868-880

Author(s):

Nguyen Hong Quan ◽

Nguyen Thuy Binh ◽

Tran Duc Long ◽

Le Thi Lan

Keyword(s):

Deep Learning ◽

Video Analysis ◽

Camera Network ◽

Unified Framework ◽

Person Detection ◽

Practical Applications ◽

Detection And Tracking ◽

Analysis System ◽

Bounding Boxes

Along with the strong development of camera networks, a video analysis system has been become more and more popular and has been applied in various practical applications. In this paper, we focus on person re-identification (person ReID) task that is a crucial step of video analysis systems. The purpose of person ReID is to associate multiple images of a given person when moving in a non-overlapping camera network. Many efforts have been made to person ReID. However, most of studies on person ReID only deal with well-alignment bounding boxes which are detected manually and considered as the perfect inputs for person ReID. In fact, when building a fully automated person ReID system the quality of the two previous steps that are person detection and tracking may have a strong effect on the person ReID performance. The contribution of this paper are two-folds. First, a unified framework for person ReID based on deep learning models is proposed. In this framework, the coupling of a deep neural network for person detection and a deep-learning-based tracking method is used. Besides, features extracted from an improved ResNet architecture are proposed for person representation to achieve a higher ReID accuracy. Second, our self-built dataset is introduced and employed for evaluation of all three steps in the fully automated person ReID framework.

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Deep Learning Approach to Face Pose Estimation for High-Speed Camera Network System

2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) ◽

10.1109/icaiic48513.2020.9065051 ◽

2020 ◽

Author(s):

Seohyun Lee ◽

Hyuno Kim ◽

Masatoshi Ishikawa

Keyword(s):

Deep Learning ◽

Pose Estimation ◽

High Speed ◽

Learning Approach ◽

Network System ◽

High Speed Camera ◽

Camera Network

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Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

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Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

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