Review on Human Re-identification with Multiple Cameras

Human re-identification is the core task in most surveillance systems and it is aimed at matching human pairs from different non-overlapping cameras. There are several challenging issues that need to be overcome to achieve reidentification, such as overcoming the variations in viewpoint, pose, image resolution, illumination and occlusion. In this study, we review existing works in human re-identification task. Advantages and limitations of recent works are discussed. At the end, this paper suggests some future research directions for human re-identification

Modeling feature distances by orientation driven classifiers for person re-identification

6siTo tackle the re-identification challenges existing methods propose to directly match image features or to learn the transformation of features that undergoes between two cameras. Other methods learn optimal similarity measures. However, the performance of all these methods are strongly dependent from the person pose and orientation. We focus on this aspect and introduce three main contributions to the field: (i) to propose a method to extract multiple frames of the same person with different orientations in order to capture the complete person appearance; (ii) to learn the pairwise feature dissimilarities space (PFDS) formed by the subspaces of similar and different image pair orientations; and (iii) within each subspace, a classifier is trained to capture the multi-modal inter-camera transformation of pairwise image dissimilarities and to discriminate between positive and negative pairs. The experiments show the superior performance of the proposed approach with respect to state-of-the-art methods using two publicly available benchmark datasets. © 2016 Elsevier Inc. All rights reserved.partially_openopenGarcía, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, ChristianGarcía, Jorge; Martinel, Niki; Gardel, Alfredo; Bravo, Ignacio; Foresti, Gian Luca; Micheloni, Christia

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream CNN that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for OCR to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the two-stream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https://github.com/icarofua/vehicle-rear

A Watch-List Based Classification System

Watch-list-based classification and verification is advantageous in a variety of surveillance applications. In this thesis, we present an approach for verifying if a query image lies in a predefined set of target samples (the watch-list) or not. This approach is particularly useful at identifying a small set of target subjects and therefore can render high levels of accuracy. Further, this approach can also be extended to identify the query image exactly out of the target samples. The three- stages approach proposed here consists of using a combination of color and texture features to represent the image and further using, Kernel Partial Least Squares for dimensionality reduction followed by a classifier. This approach provides improved accuracy as shown by experiments on two datasets

People detection and re-identification for multi surveillance cameras

International audienceRe-identifying people in a network of non overlapping cameras requires people to be accurately detected and tracked in order to build a strong visual signature of people appearances. Traditional surveillance cameras do not provide high enough image resolution to iris recognition algorithms. State of the art face recognition can not be easily applied to surveillance videos as people need to be facing the camera at a close range. The different lighting environment contained in each camera scene and the strong illumination variability occurring as people walk throughout a scene induce great variability in their appearance. %In addition, surveillance scene often display people whose images occlud each other onto the image plane making people detection difficult to achieve. In addition, people images occlud each other onto the image plane making people detection difficult to achieve. We propose a novel simplified Local Binary Pattern features to detect people, head and faces. A Mean Riemannian Covariance Grid (MRCG) is used to model appearance of tracked people to obtain highly discriminative human signature. The methods are evaluated and compared with the state of the art algorithms. We have created a new dataset from a network of 2 cameras showing the usefulness of our system to detect, track and re-identify people using appearance and face features

Multiple-shot Human Re-Identification by Mean Riemannian Covariance Grid

International audienceHuman re-identification is defined as a requirement to determine whether a given individual has already appeared over a network of cameras. This problem is particularly hard by significant appearance changes across different camera views. In order to re-identify people a human signature should handle difference in illumination, pose and camera parameters. We propose a new appearance model combining information from multiple images to obtain highly discriminative human signature, called Mean Riemannian Covariance Grid (MRCG). The method is evaluated and compared with the state of the art using benchmark video sequences from the ETHZ and the i-LIDS datasets. We demonstrate that the proposed approach outperforms state of the art methods. Finally, the results of our approach are shown on two other more pertinent datasets

The Neural Representation of Personally Familiar and Unfamiliar Faces in the Distributed System for Face Perception

Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception