Unsupervised learning of generative topic saliency for person re-identification

(c) 2014. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.© 2014. The copyright of this document resides with its authors. Existing approaches to person re-identification (re-id) are dominated by supervised learning based methods which focus on learning optimal similarity distance metrics. However, supervised learning based models require a large number of manually labelled pairs of person images across every pair of camera views. This thus limits their ability to scale to large camera networks. To overcome this problem, this paper proposes a novel unsupervised re-id modelling approach by exploring generative probabilistic topic modelling. Given abundant unlabelled data, our topic model learns to simultaneously both (1) discover localised person foreground appearance saliency (salient image patches) that are more informative for re-id matching, and (2) remove busy background clutters surrounding a person. Extensive experiments are carried out to demonstrate that the proposed model outperforms existing unsupervised learning re-id methods with significantly simplified model complexity. In the meantime, it still retains comparable re-id accuracy when compared to the state-of-the-art supervised re-id methods but without any need for pair-wise labelled training data

Person re-identification using deep foreground appearance modeling

Person reidentification is the process of matching individuals from images taken of them at different times and often with different cameras. To perform matching, most methods extract features from the entire image; however, this gives no consideration to the spatial context of the information present in the image. We propose using a convolutional neural network approach based on ResNet-50 to predict the foreground of an image: the parts with the head, torso, and limbs of a person. With this information, we use the LOMO and salient color name feature descriptors to extract features primarily from the foreground areas. In addition, we use a distance metric learning technique (XQDA), to calculate optimally weighted distances between the relevant features. We evaluate on the VIPeR, QMUL GRID, and CUHK03 data sets and compare our results against a linear foreground estimation method, and show competitive or better overall matching performance

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201

People re-identification using deep appearance, feature and attribute learning

Person Re-Identification (Re-ID) is the act of matching one or more query images of an individual with images of the same individual in a gallery set. We propose various methods to improve Re-ID performance via foreground modelling, skeleton prediction and attribute detection. Foreground modelling is an important preprocessing step in Re-ID, allowing more representative features to be extracted. We propose two foreground modelling methods which learn a mapping between a set of training images and skeleton keypoints. The first utilises Partial Least Squares (PLS) regression to learn a mapping between Histogram of Oriented Gradients (HOG) features extracted from person images, and skeleton keypoints. The second instead learns the mapping using a deep convolutional neural network (CNN). Using a CNN has been shown to generalise better, particularly for unusual pedestrian poses. We then utilise the predicted skeleton to generate a binary mask, separating the foreground from the background. This is useful for weighting image features extracted from foreground areas higher than those extracted from background areas. We apply this weighting during the feature extraction stage to increase matching rates. The predicted skeleton can be used to divide a pedestrian image into multiple parts, such as head and torso. We propose using the divided images as input to an attribute prediction network. We then use this network to generate robust feature descriptors, and demonstrate competitive Re-ID matching rates. We evaluate on a number of dfferent Re-ID data sets, each possessing significant variations in visual characteristics. We validate our proposals by measuring the rank-n score, which is equivalent to the percentage of identities correctly predicted within n attempts. We evaluate our skeleton prediction network using root mean square error (RMSE), and our attribute prediction network using accuracy. Experiments demonstrate that our proposed methods can supplement traditional Re-ID approaches to increase rank-n matching rates

Agile and Attached: The Impact of Agile Practices on Agile Team Members’ Affective Organisational Commitment

The current shortage of information systems (IS) specialists is leading to a strongly competitive labour market for the IT workforce. Technology companies need opportunities to prevent high replacement costs and knowledge loss by strengthening the affective organisational commitment (affective OC) of their employees. Using structural equation modelling, we investigate the influence of agile information systems development (ISD) on team members’ affective OC. Our results demonstrate that agile project management (APM)positively predicts affective OC directly as well as indirectly via team members’ job autonomy (JA) and their supervisors’ support (SS). Our study gives empirical evidence on the relationship between agile ISD practices and affective OC and provides implications how to successfully leverage team members’ affective OC. For practitioners, our research expounds why and how agile ISD is a suitable instrument to transform leadership culture within the company so as to raise affective OC beyond the IT workforce

Face De-Identification for Privacy Protection

The ability to record, store and analyse images of faces economically, rapidly and on a vast scale brings people’s attention to privacy. The current privacy protection approaches for face images are mainly through masking, blurring or black-out which, however, removes data utilities along with the identifying information. As a result, these ad hoc methods are hardly used for data publishing or in further researches. The technique of de-identification attempts to remove identifying information from a dataset while preserving the data utility as much as possible. The research on de-identify structured data has been established while it remains a challenge to de-identify unstructured data such as face data in images and videos. The k-Same face de-identification was the first method that attempted to use an established de-identification theory, k-anonymity, to de-identify a face image dataset. The k-Same face de-identification is also the starting point of this thesis. Re-identification risk and data utility are two incompatible aspects in face de-identification. The focus of this thesis is to improve the privacy protection performance of a face de-identification system while providing data utility preserving solutions for different application scenarios. This thesis first proposes the k-Same furthest face de-identification method which introduces the wrong-map protection to the k-Same-M face de-identification, where the identity loss is maximised by replacing an original face with the face that has the least similarity to it. The data utility of face images has been considered from two aspects in this thesis, the dataset-wise data utility such as data distribution of the data set and the individual-wise data utility such as the facial expression in an individual image. With the aim to preserve the diversity of a face image dataset, the k-Diff-furthest face de-identification method is proposed, which extends the k-Same-furthest method and can provide the wrong-map protection. With respect to the data utility of an individual face image, the visual quality and the preservation of facial expression are discussed in this thesis. A method to merge the isolated de-identified face region and its original image background is presented. The described method can increase the visual quality of a de-identified face image in terms of fidelity and intelligibility. A novel solution to preserving facial expressions in de-identified face images is presented, which can preserve not only the category of facial expressions but also the intensity of face Action Units. Finally, an integration of the Active Appearance Model (AAM) and Generative Adversarial Network (GAN) is presented, which achieves the synthesis of realistic face images with shallow neural network architectures

A confirmatory composite analysis for the Italian validation of the interactions anxiousness scale: a higher-order version

This study examined the factor structure and model specifications of the Interaction Anxiousness Scale (IAS) with confirmatory composite analysis (CCA) using partial least squares-structural equation modeling (PLS-SEM) with a sample of Italian adolescents (n=764). The CCA and PLS-SEM results identified the reflective nature of the IAS sub-scale scores, supporting an alternative measurement model of the IAS scores as a second-order reflective–reflective model