The ever-expanding urbanization and the advent of smart cities need better crowd management and security surveillance systems. Advanced systems are required to improve and automate the crowd management system. The aim of the closed circuit television and visual monitoring systems using multiple cameras faces many challenges like illumination variance, occlusion and small spatial–temporal resolution, person in sleep, shadows, dynamic backgrounds, and noises. Therefore, the crowd monitoring, prevention of stampedes and crowd-related emergencies in the smart cities are major challenging problems. In this paper, we propose an intelligent decision computing based paradigm for crowd monitoring in the smart city. In the intelligent computing based framework, the optimization algorithm is applied to compute the feature of crowd motion and measure the correlation between agents based motion model and the crowd data using extended Kalman filtering approach and KL-divergence technique. The proposed framework measures the correlation measure based on extracted novel distinctive feature, and holistic feature of crowd data represent and to classify the crowd motion of individual. Our experimental results demonstrate that the proposed approach yields 96.20% average precision in classifying real-world highly dense crowd scenes. © 2017 Elsevier Inc.

Arun Kumar S

Department of Information Security

School of Computer Science and Engineering

Vellore Campus

Kumar S

Datta D

Singh S.K

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Journal of Parallel and Distributed Computing

Fulltext

Journal of Big Data

Intelligent video surveillance: a review through deep learning techniques for crowd analysis

Code obfuscation is a set of transformations that make code programs harder to understand. The goal of code obfuscation is to make reverse engineering of programs infeasible, while maintaining the logic on the program. Originally, it has been used to protect intellectual property. However, recently code obfuscation has been also used by malware writers in order to make cyber threats easily able to evade antimalware scanners. As a matter of fact, metamorphic and polymorphic viruses exhibit the ability to obfuscate their code as they propagate. In this paper we propose a model checking-based approach which is able to identify the most widespread obfuscating techniques, without making any assumptions about the nature of the obfuscations used. We evaluate the proposed method on a real-world dataset obtaining an accuracy equal to 0.9 in the identification of obfuscation techniques. © 2018 Elsevier Inc.

Evaluating model checking for cyber threats code obfuscation identification

With the prevalent use of mobile cameras to capture images, the demands for efficient and effective methods for indexing and retrieval of personal image collections on mobile devices have also risen. In this paper, we propose to represent images with hash codes, which is a compressed representation of deep convolutional features using deep auto-encoder on the cloud. To ensure user's privacy, the image is first encrypted using a light-weight encryption algorithm on mobile device prior to offloading it to the cloud for features extraction. This approach eliminates the computationally expensive process of features extraction on resource constrained devices. A pre-trained convolutional neural network (CNN) is used to extract features which are then transformed to compact binary codes using a deep auto-encoder. The hash codes are then sent back to the mobile device where they are stored in a hash table along with image location. Approximate nearest neighbor (ANN) search approach is utilized to efficiently retrieve the desired images without exhaustive searching of the entire image collection. The proposed method is evaluated against three different publicly available image datasets namely Corel-10K, GHIM-10K, and Product image dataset. Experimental results demonstrate that features representation using CNN and auto-encoder shows much better results than several state-of-the-art hashing schemes for image retrieval on mobile devices. © 2018 Elsevier B.V.

Computer Communications

Privacy-preserving image retrieval for mobile devices with deep features on the cloud

Future Generation Computer Systems

Monitoring of pet animal in smart cities using animal biometrics

IET Image Processing

Muzzle point pattern based techniques for individual cattle identification

Physica A: Statistical Mechanics and its Applications

An entropy model to measure heterogeneity of pedestrian crowds using self-propelled agents

Journal of Internet Services and Applications

Smart cities – enabling services and applications

IEEE Transactions on Multimedia

Exemplar-AMMs: Recognizing Crowd Movements From Pedestrian Trajectories

An intelligent decision computing paradigm for crowd monitoring in the smart city

Journal	Data powered by TypesetJournal of Parallel and Distributed Computing
Publisher	Data powered by TypesetElsevier BV
ISSN	0743-7315
Open Access	0