Remote feature learning for mobile re-identification

Vernier, Marco; Martinel, Niki; Micheloni, Christian; Foresti, Gian Luca

doi:10.1109/icdsc.2013.6778221

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Liu et al [36] provided extensive study of feature importance for person reidentification and proposed a method for on-the-fly mining of feature. For person reidentification on mobile devices, Vernier et al [37] introduced a client-server system that improved the reidentification performance over time with reduced computation time.…”

Section: A Related Workmentioning

confidence: 99%

Person Reidentification With Reference Descriptor

Kafai

Yang

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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Person identification across nonoverlapping cameras, also known as person reidentification, aims to match people at different times and locations. Reidentifying people is of great importance in crucial applications such as widearea surveillance and visual tracking. Due to the appearance variations in pose, illumination, and occlusion in different camera views, person reidentification is inherently difficult. To address these challenges, a reference-based method is proposed for person reidentification across different cameras. Instead of directly matching people by their appearance, the matching is conducted in a reference space where the descriptor for a person is translated from the original color or texture descriptors to similarity measures between this person and the exemplars in the reference set. A subspace is first learned in which the correlations of the reference data from different cameras are maximized using regularized canonical correlation analysis (RCCA). For reidentification, the gallery data and the probe data are projected onto this RCCA subspace and the reference descriptors (RDs) of the gallery and probe are generated by computing the similarity between them and the reference data. The identity of a probe is determined by comparing the RD of the probe and the RDs of the gallery. A reranking step is added to further improve the results using a saliency-based matching scheme. Experiments on publicly available datasets show that the proposed method outperforms most of the state-of-the-art approaches.

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Section: A Related Workmentioning

confidence: 99%

Person Reidentification With Reference Descriptor

Kafai

Yang

et al. 2016

IEEE Trans. Circuits Syst. Video Technol.

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“…In spite of growing interest in re-identifying people in a non-overlapping embedded smart cameras (e.g., mobile phone), there are limited works that address person mobile reidentification [2,6]. Since no data sets including videos captured by mobile devices are publicly available.…”

Section: Introductionmentioning

confidence: 98%

“…To address these problems, uncalibrated disjoint cameras (namely embedded smart cameras, i.e., videos captured by embedded cameras without any knowledge of camera placement or device setting) are now being employed for person re-identification [2,6]. In spite of growing interest in re-identifying people in a non-overlapping embedded smart cameras (e.g., mobile phone), there are limited works that address person mobile reidentification [2,6].…”

Section: Introductionmentioning

confidence: 98%

Mobile Re-Identification Based on Local Features Analysis

Akhtar

Martinel

Micheloni

et al. 2014

Proceedings of the International Conference on Distributed Smart Cameras

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Nowadays, there is a growing interest in security applications using embedded smart cameras. Despite the rising attention to re-identifying the people in a non-overlapping embedded camera network, there exist no comparative evaluation of the existing schemes, specially facial representations. Though, facial features offer the advantage of remaining stable over much larger time intervals in contrast to commonly exploited features for person re-identification, such as whole body appearance. However, the challenge in using faces for such applications, apart from low captured face resolutions, is that their appearance is largely influenced by changes in illumination, pose, viewpoint, background and partial occlusions. Therefore, in this paper, we present a comparative study of facial local features for the task of person re-identification using embedded smart cameras in a realistic surveillance scenario. The experiments are performed on a novel data set which we make publicly available for future research in this area. The contributions of this work are as follows: 1) evaluation of eight baseline facial local features for person re-identification using embedded smart cameras; 2) an empirical study of the performance of person re-identification systems based on local facial features when fused with auxiliary information such as face shape, face skin color tone, hair color, and cloth color; 3) comparative study of single-and multi-shot approaches; 4) a novel data set using embedded cameras for person re-identification with properties unavailable in exiting data sets.

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“…In the first approach, the re-identification is attacked by means of a discriminative signature based method [3,4,5,6]. Given an image, the person body parts are detected using camera specific learned models, then local and global features are adopted to create a discriminative person signature.…”

Section: Distributed Person Re-identificationmentioning

confidence: 99%

On a Distributed Video Surveillance System to Track Persons in Camera Networks

Martinel

2015

ELCVIA

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Contribution of the ThesisIn automated video surveillance applications, presenting the useful information to the human operators is a challenging task. Current systems usually require a prohibitive amount of human resources and lead to a quick decrease of the attention of the human operators through time, thus preventing them to catch the relevant events that may be worth to further investigate.In addition, when monitoring a wide area, it becomes hard to deploy a network of video sensors such that there are enough overlapping FoVs to cover every point of the environment. This leads to the development of video surveillance systems (VSS) that provide partial area coverage. As a result, "blind-gaps" between camera FoVs are introduced. One of the most interesting problems which such "blind-gaps" bring in is to re-identify the persons moving across disjoint FoVs.The contribution of the thesis * is two-fold. First, an advanced VSS is designed to display the proper taskdependent information to operators that are monitoring a wide area. In particular, the system helps operators in the task of tracking persons across camera views. This raised the need for a system capable of re-identify the subjects moving through disjoint FoVs. This leads to the second contribution of the thesis, a distributed approach to address the challenges of the person re-identification problem. Adaptive Human Interface for VSSThe main goal of the proposed VSS is the development of an effective information visualization technique that properly displays only the most relevant cameras and information contents to simplify the operators' tracking tasks [1,2]. The key idea is to visualize only most probable streams, i.e. those that will be involved with the motion of the tracked persons. Towards this goal we propose a novel dynamic organization, activation and switching of the User Interface (UI) elements based on the output of video analytics algorithms.The first objective is to distill the volumes of monitoring information into a human manageable quantity. This is achieved by introducing an hand-off task between different camera views so that a single person can be tracked across different FoVs. The proposed camera planning algorithm uses geographical clues and exploits the predicted trajectories to build an accurate camera activation plan.

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Remote feature learning for mobile re-identification

Cited by 7 publications

References 12 publications

Person Reidentification With Reference Descriptor

Person Reidentification With Reference Descriptor

Mobile Re-Identification Based on Local Features Analysis

On a Distributed Video Surveillance System to Track Persons in Camera Networks

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