Pedestrian Attribute Recognition: A Survey

Recognizing pedestrian attributes is an important task in computer vision community due to it plays an important role in video surveillance. Many algorithms has been proposed to handle this task. The goal of this paper is to review existing works using traditional methods or based on deep learning networks. Firstly, we introduce the background of pedestrian attributes recognition (PAR, for short), including the fundamental concepts of pedestrian attributes and corresponding challenges. Secondly, we introduce existing benchmarks, including popular datasets and evaluation criterion. Thirdly, we analyse the concept of multi-task learning and multi-label learning, and also explain the relations between these two learning algorithms and pedestrian attribute recognition. We also review some popular network architectures which have widely applied in the deep learning community. Fourthly, we analyse popular solutions for this task, such as attributes group, part-based, \emph{etc}. Fifthly, we shown some applications which takes pedestrian attributes into consideration and achieve better performance. Finally, we summarized this paper and give several possible research directions for pedestrian attributes recognition. The project page of this paper can be found from the following website: \url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey: https://sites.google.com/view/ahu-pedestrianattributes

New delay-dependent stability criteria for recurrent neural networks with time-varying delays

Dimirovski, Georgi M. (Dogus Author)This work is concerned with the delay-dependentstability problem for recurrent neural networks with time-varying delays. A new improved delay-dependent stability criterion expressed in terms of linear matrix inequalities is derived by constructing a dedicated Lyapunov-Krasovskii functional via utilizing Wirtinger inequality and convex combination approach. Moreover, a further improved delay-dependent stability criterion is established by means of a new partitioning method for bounding conditions on the activation function and certain new activation function conditions presented. Finally, the application of these novel results to an illustrative example from the literature has been investigated and their effectiveness is shown via comparison with the existing recent ones

Effect of deletion of the rgpA gene on selected virulence of Porphyromonas gingivalis

AbstractBackground/purposeThe most potent virulence factors of the periodontal pathogen Porphyromonas gingivalis are gingipains, three cysteine proteases (RgpA, RgpB, and Kgp) that bind and cleave a wide range of host proteins. Considerable proof indicates that RgpA contributes to the entire virulence of the organism and increases the risk of periodontal disease by disrupting the host immune defense and destroying the host tissue. However, the functional significance of this proteinase is incompletely understood. It is important to analyze the effect of arginine-specific gingipain A gene (rgpA) on selected virulence and physiological properties of P. gingivalis.Materials and methodsElectroporation and homologous recombination were used to construct an rgpA mutant of P. gingivalis ATCC33277. The mutant was verified by polymerase chain reaction and sodium dodecyl sulfate–polyacrylamide gel electrophoresis. Cell structures of the mutant were examined by transmission electron microscopy and homotypic biofilm formation was examined by confocal laser scanning microscopy.ResultsGene analysis revealed that the rgpA gene was deleted and replaced by a drug resistance gene marker. The defect of the gene resulted in a complete loss of RgpA proteinase, a reduction of out membrane vesicles and hemagglutination, and an increase in homotypic biofilm formation.ConclusionOur data indicate that an rgpA gene deficient strain of P. gingivalis is successfully isolated. RgpA may have a variety of physiological and pathological roles in P. gingivalis

A Monte Carlo Bayesian Search for the Plausible Source of the Telescope Array Hotspot

The Telescope Array (TA) collaboration has reported a hotspot of 19 ultrahigh-energy cosmic rays (UHECRs). Using a universal model with one source and energy-dependent magnetic deflections, we show that the distribution of the TA hotspot events is consistent with a single source hypothesis, although multiple sources cannot be ruled out. The chance probability of this distribution arising from a homogeneous distribution is $0.2\%$. We describe a Monte Carlo Bayesian (MCB) inference approach, which can be used to derive parameters of the magnetic fields as well as the source coordinates, and we apply this method to the TA hotspot data, inferring the location of the likely source. We discuss possible applications of the same approach to future data.Comment: 7 pages, 5 figures, accepted by Physical Review