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http://romisatriawahono.net/lecture/rm/survey/computer%20vision/Gala%20-%20Person%20Reidentification%20-%202014.pdf

Editor's Choice Article: A survey of approaches and trends in person re-identification

Considering the increase of urban population and traffic congestion, smart parking is always a strategic issue to work on, not only in the research field, but also from economic interests. Thanks to information and communication technology evolution, drivers can more efficiently find satisfying parking spaces with smart parking services. The existing and ongoing works on smart parking are complicated and transdisciplinary. While deploying a smart parking system, cities, as well as urban engineers, need to spend a very long time to survey and inspect all the possibilities. Moreover, many varied works involve multiple disciplines, which are closely linked and inseparable. To give a clear overview, we introduce a smart parking ecosystem and propose a comprehensive and thoughtful classification by identifying their functionalities and problematic focuses. We go through the literature over the period of 2000–2016 on parking solutions as they were applied to smart parking development and evolution, and propose three macro-themes: information collection, system deployment, and service dissemination. In each macro-theme, we explain and synthesize the main methodologies used in the existing works and summarize their common goals and visions to solve current parking difficulties. Finally, we give our engineering insights and show some challenges and open issues. Our survey gives an exhaustive study and a prospect in a multidisciplinary approach. Besides, the main findings of the current state-of-the-art throw out recommendations for future research on smart cities and the Internet architecture.

/pdf/a-survey-of-smart-parking-solutions-29hqwxlnoj.pdf

A Survey of Smart Parking Solutions

The field of surveillance and forensics research is currently shifting focus and is now showing an ever increasing interest in the task of people reidentification. This is the task of assigning the same identifier to all instances of a particular individual captured in a series of images or videos, even after the occurrence of significant gaps over time or space. People reidentification can be a useful tool for people analysis in security as a data association method for long-term tracking in surveillance. However, current identification techniques being utilized present many difficulties and shortcomings. For instance, they rely solely on the exploitation of visual cues such as color, texture, and the object’s shape. Despite the many advances in this field, reidentification is still an open problem. This survey aims to tackle all the issues and challenging aspects of people reidentification while simultaneously describing the previously proposed solutions for the encountered problems. This begins with the first attempts of holistic descriptors and progresses to the more recently adopted 2D and 3D model-based approaches. The survey also includes an exhaustive treatise of all the aspects of people reidentification, including available datasets, evaluation metrics, and benchmarking.

People reidentification in surveillance and forensics: A survey

We propose an automatic, privacy-preserving, fall detection method for indoor environments, based on the usage of the Microsoft Kinect® depth sensor, in an “on-ceiling” configuration, and on the analysis of depth frames. All the elements captured in the depth scene are recognized by means of an Ad-Hoc segmentation algorithm, which analyzes the raw depth data directly provided by the sensor. The system extracts the elements, and implements a solution to classify all the blobs in the scene. Anthropometric relationships and features are exploited to recognize one or more human subjects among the blobs. Once a person is detected, he is followed by a tracking algorithm between different frames. The use of a reference depth frame, containing the set-up of the scene, allows one to extract a human subject, even when he/she is interacting with other objects, such as chairs or desks. In addition, the problem of blob fusion is taken into account and efficiently solved through an inter-frame processing algorithm. A fall is detected if the depth blob associated to a person is near to the floor. Experimental tests show the effectiveness of the proposed solution, even in complex scenarios.

https://www.mdpi.com/1424-8220/14/2/2756/pdf

A depth-based fall detection system using a Kinect® sensor.

Most current algorithms for automatic glaucoma assessment using fundus images rely on handcrafted features based on segmentation, which are affected by the performance of the chosen segmentation method and the extracted features. Among other characteristics, convolutional neural networks (CNNs) are known because of their ability to learn highly discriminative features from raw pixel intensities. In this paper, we employed five different ImageNet-trained models (VGG16, VGG19, InceptionV3, ResNet50 and Xception) for automatic glaucoma assessment using fundus images. Results from an extensive validation using cross-validation and cross-testing strategies were compared with previous works in the literature. Using five public databases (1707 images), an average AUC of 0.9605 with a 95% confidence interval of 95.92–97.07%, an average specificity of 0.8580 and an average sensitivity of 0.9346 were obtained after using the Xception architecture, significantly improving the performance of other state-of-the-art works. Moreover, a new clinical database, ACRIMA, has been made publicly available, containing 705 labelled images. It is composed of 396 glaucomatous images and 309 normal images, which means, the largest public database for glaucoma diagnosis. The high specificity and sensitivity obtained from the proposed approach are supported by an extensive validation using not only the cross-validation strategy but also the cross-testing validation on, to the best of the authors’ knowledge, all publicly available glaucoma-labelled databases. These results suggest that using ImageNet-trained models is a robust alternative for automatic glaucoma screening system. All images, CNN weights and software used to fine-tune and test the five CNNs are publicly available, which could be used as a testbed for further comparisons.

/pdf/cnns-for-automatic-glaucoma-assessment-using-fundus-images-1wgr22628q.pdf

CNNs for automatic glaucoma assessment using fundus images: an extensive validation

This paper presents a video-based approach to detect the presence of parked vehicles in street lanes. Potential applications include the detection of illegally and double-parked vehicles in urban scenarios and incident detection on roads. The technique extracts information from low-level feature points (Harris corners) to create spatiotemporal maps that describe what is happening in the scene. The method neither relies on background subtraction nor performs any form of object tracking. The system has been evaluated using private and public data sets and has proven to be robust against common difficulties found in closed-circuit television video, such as varying illumination, camera vibration, the presence of momentary occlusion by other vehicles, and high noise levels.

/pdf/detection-of-parked-vehicles-using-spatiotemporal-maps-3bx4m7ahbf.pdf

Detection of Parked Vehicles Using Spatiotemporal Maps

This study proposes the concept of bodyprints to perform re-identification of people in surveillance videos. Bodyprints are obtained using calibrated depth-colour cameras such as kinect. The author's results on a database of 40 people show that bodyprints are very robust to changes of pose, point of view and illumination. Potential applications include tracking people with networks of non-overlapping cameras.

/pdf/who-is-who-at-different-cameras-people-re-identification-4m0euqgvgy.pdf

Who is who at different cameras: people re-identification using depth cameras

This paper presents an approach to estimate traffic queue lengths. It does not rely on any sort of object tracking or background subtraction. The method is based on the detection of low level-features (corners) which can be associated to the presence of vehicles. Then, features are classified as moving or static. An analysis of the locations of static corners allows to measure queue lengths in meters. Errors are smaller than vehicle lengths. The algorithm has been tested with around the clock recordings in different real scenarios.

Video-based traffic queue length estimation

This paper compares several approaches to extract facial landmarks and studies their influence on face recognition problems. In order to obtain fair comparisons, we use the same number of facial landmarks and the same type of descriptors (HOG descriptors) for each approach. The comparative results are obtained using FERET and FRGC datasets and show that better recognition rates are obtained when landmarks are located at real facial fiducial points. However, if the automatic detection of these is compromised by the difficulty of the images, better results are obtained using fixed landmarks grids.

Jose M. Mossi

Papers

CNNs for automatic glaucoma assessment using fundus images: an extensive validation

Detection of Parked Vehicles Using Spatiotemporal Maps

Who is who at different cameras: people re-identification using depth cameras

Video-based traffic queue length estimation

A Comparative Study of Facial Landmark Localization Methods for Face Recognition Using HOG descriptors