A Novel Visual Word Co-occurrence Model for Person Re-identification

Person re-identification aims to maintain the identity of an individual in diverse locations through different non-overlapping camera views. The problem is fundamentally challenging due to appearance variations resulting from differing poses, illumination and configurations of camera views. To deal with these difficulties, we propose a novel visual word co-occurrence model. We first map each pixel of an image to a visual word using a codebook, which is learned in an unsupervised manner. The appearance transformation between camera views is encoded by a co-occurrence matrix of visual word joint distributions in probe and gallery images. Our appearance model naturally accounts for spatial similarities and variations caused by pose, illumination & configuration change across camera views. Linear SVMs are then trained as classifiers using these co-occurrence descriptors. On the VIPeR and CUHK Campus benchmark datasets, our method achieves 83.86% and 85.49% at rank-15 on the Cumulative Match Characteristic (CMC) curves, and beats the state-of-the-art results by 10.44% and 22.27%.Comment: Accepted at ECCV Workshop on Visual Surveillance and Re-Identification, 201

Video surveillance systems-current status and future trends

Within this survey an attempt is made to document the present status of video surveillance systems. The main components of a surveillance system are presented and studied thoroughly. Algorithms for image enhancement, object detection, object tracking, object recognition and item re-identification are presented. The most common modalities utilized by surveillance systems are discussed, putting emphasis on video, in terms of available resolutions and new imaging approaches, like High Dynamic Range video. The most important features and analytics are presented, along with the most common approaches for image / video quality enhancement. Distributed computational infrastructures are discussed (Cloud, Fog and Edge Computing), describing the advantages and disadvantages of each approach. The most important deep learning algorithms are presented, along with the smart analytics that they utilize. Augmented reality and the role it can play to a surveillance system is reported, just before discussing the challenges and the future trends of surveillance

Exploiting Multiple Detections for Person Re-Identification

Re-identification systems aim at recognizing the same individuals in multiple cameras, and one of the most relevant problems is that the appearance of same individual varies across cameras due to illumination and viewpoint changes. This paper proposes the use of cumulative weighted brightness transfer functions (CWBTFs) to model these appearance variations. Different from recently proposed methods which only consider pairs of images to learn a brightness transfer function, we exploit such a multiple-frame-based learning approach that leverages consecutive detections of each individual to transfer the appearance. We first present a CWBTF framework for the task of transforming appearance from one camera to another. We then present a re-identification framework where we segment the pedestrian images into meaningful parts and extract features from such parts, as well as from the whole body. Jointly, both of these frameworks contribute to model the appearance variations more robustly. We tested our approach on standard multi-camera surveillance datasets, showing consistent and significant improvements over existing methods on three different datasets without any other additional cost. Our approach is general and can be applied to any appearance-based metho

Statistical models to predict exposure settings using two different iPhone camera apps

The StegoAppDB [Newman, J. (2019)] is a digital image database containing camera data from Android and iPhone mobile phones and developed for forensic purposes. Taken with a custom-designed camera app called Cameraw rather than the camera app native to the mobile device, it is not known what relation exists between the exposure settings of images taken with Cameraw and those with the native app. This knowledge would provide the digital image forensic analyst more information to answer this question: are the images in the database representative of images encountered in forensic settings? To this end, this thesis provides results from experiments designed to model the relation between exposure settings between images taken from the two camera apps.For purposes of this thesis, the term exposure settings denotes the exposure time and the ISO value excluding the lens aperture variable, as that last variable is fixed on a mobile phone.In this thesis, images acquired from four iOS devices - an iPhone 7, iPhone 8, and two iPhone Xs - are analyzed to develop regression models that fit exposure settings from image data for each device. Specific image acquisition experiments are designed to collect pairs of images very close in time and space from each of the two apps so that their exposure settings could be compared.A broad range of ISO and exposure time values are collected to represent a variety of exposure settings possible on a mobile device. Several different regression models with cross validation are developed for the data from each phone, and generalized linear models are also applied. Errors for the training, validation and testing sets are used to evaluate the performance of individual models,and the adjusted R-squared statistic is used to compare performances across models. The best models with respect to the performance measures are identified for each type of analysis and for each iPhone.The results show that most of the linear models typically model the data fairly well, and exposure settings can be predicted from the models. One notable exception is the iPhone 7: the best models for the iPhone 7 are different because the exposure setting data differs significantly from the other two iPhone models\u27 exposure setting data. The results in this thesis show that in a very limited case, for these four devices, the Cameraw app can be a reliable alternative to the native camera app