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In an intelligent surveillance system, detecting and predicting diverse collective crowd behaviors has emerged as a challenging problem for efficient crowd management. In real-world scenarios, potential disasters and hazards can be averted by considering crowd psychology for predicting crowd behaviors. This paper proposes an approach that exploits the psychological and cognitive aspects of human behavior in determining nine diverse crowd behaviors. The proposed approach is a combination of two cognitive deep learning frameworks and a psychological fuzzy computational model that utilizes OCC theory of emotions, OCEAN five-factor model of personality and visual attention for detecting crowd behaviors. Experiments are performed on different datasets and the results prove that our approach is successful in detecting and predicting crowd behavior in confronting situations and also outperforms the state-of-the-art methods. In particular, considering psychological aspects and cognition in determining crowd behavior is beneficial for rectifying the semantic ambiguity in identifying crowd behaviors.

Modern Internet of Things (IoT)-assisted networks deploy many critical computing devices in the edge of the network so as to mitigate the bottleneck of network latency and bandwidth outage. Such computing devices have become attractive targets of attackers, as they derive critical insights that can often regulate the underlying industrial processes. Security administrators have to analyze vulnerable configurations, and evaluate feasible hardening options to secure such systems. In this article, we investigate the security threats to the edge devices in the IoT-assisted networks because of the inherent vulnerabilities present in the IoT devices, and formulate a novel graphical security model to conduct vulnerability-based risk assessment in such networks. We further propose a set of combinatorial optimization techniques for security hardening. For securing the target devices, we propose a randomized algorithm to enforce isolation of potential target devices from the attackers. The algorithm produces the least number of vulnerabilities, the removal of which protects the targets. For densely connected networks, we propose another algorithm to identify a minimal set of vulnerabilities to be hardened so as to keep potential targets below admissible levels of risk. The approach includes the formulation of an integer linear program, which is relaxed to solve in polynomial time, followed by a round-off technique to find the actual solution.

Social networks have emerged as a fertile ground for the spread of rumors and misinformation in recent times. The increased rate of social networking owes to the popularity of social networks among the common people and user personality has been considered as a principal component in predicting individuals’ social media usage patterns. Several studies have been conducted to study the psychological factors influencing the social network usage of people but only a few works have explored the relationship between the user’s personality and their orientation to spread rumors. This research aims to investigate the effect of personality on rumor spread on social networks. In this work, we propose a psychologically-inspired fuzzy-based approach grounded on the Five-Factor Model of behavioral theory to analyze the behavior of people who are highly involved in rumor diffusion and categorize users into the susceptible and resistant group, based on their inclination towards rumor sharing. We conducted our experiments in almost 825 individuals who shared rumor tweets on Twitter related to five different events. Our study ratifies the truth that the personality traits of individuals play a significant role in rumor dissemination and the experimental results prove that users exhibiting a high degree of agreeableness trait are more engaged in rumor sharing activities and the users high in extraversion and openness trait restrain themselves from rumor propagation.

The future transportation systems demand an intelligent traffic system that can be achieved by connecting the vehicles to the Internet of Things (IoT) which in turn forms an Internet of Vehicles (I...

A crowd can be defined as a large gathering of people at a particular place showing different types of attitudes and behaviors. Monitoring and tracking these wide varieties of people is tedious in a real environment. The crowded scenarios have a high tendency to change into an abnormal condition due to sudden external pressures such as gunshots/fire or internal stress such as overcrowding, where things get often uncontrollable and the consequences are disastrous. Moreover, the use of a large number of monitoring cameras and the limited capability of human operators to analyze the video contents result in an urge to developing smart crowd monitoring systems with humanlike capabilities. This article discusses the human cognition capability and its application for smart crowd management. Cognitive computing facilitates complex task automation, real-time decision-making, predictive analytics, and processing of voluminous structured and unstructured data.

Smart and proactive surveillance of crowds has turned up into greater importance in recent years due to the increase in urban population and immutable crowd disasters. Although the behaviours that ...

In a smart surveillance system, the amount of video data has increased exponentially due to the increase in the number of monitoring devices and IoT sensors. To make smart and real-time decisions without latency in communication from these voluminous data is a tedious task. In this context, selecting informative frames from the video is of great importance that helps to extract only the salient features for further processing without latency and bandwidth constraints. In this paper, we are proposing a fast and reliable method for selecting informative frames from video sequences based on the human cognition process of visual attention to preserve the Spatio-temporal properties of the video. The proposed method extracts the informative frames using the frame informative score calculated based on visual attention maps, superpixel segmentation, and temporal information. Since our purpose is for analyzing crowd behavior from video data in a smart environment, we take two publicly available crowd video datasets for our experiments. The results show that the proposed approach is successful in extracting relevant video frames in linear time by preserving their spatial and temporal properties. We also analyze the feasibility of the proposed method in a fog computing-based simulated IoT framework, and it has been verified that the proposed cognitive approach could efficiently address the concerns of latency and bandwidth in smart surveillance environments.

Video surveillance systems substitute manual efforts in various safety critic domains such as border area, assisted living, banking, service stations, and transportation. The multimedia-based surveillance system has a significant role in security and forensic systems because people tend to be easily convinced after observing voice, image, and video. Hence, these videos are strong evidence in the forensic investigation. However, most of the criminal activities such as ATM robbery and assassination are occur at nighttime because of the crime supporting dark environment. Many of the night surveillance systems in military, as well as commercial applications, are equipped with infrared and thermal based night vision systems. Its poor capability of texture and color interpretations are the major issues to ensure secure nighttime video monitoring. Specifically, visual refinements of nighttime surroundings and foreground objects provide a valuable assistance in the nighttime security system. In this scenario, it is highly recommended a review of the state-of-the-art nighttime visual refinement approaches. We conducted an extensive literature review and classified the nighttime visual refinement approaches into nighttime restoration and enhancement. This comparative literary analysis identified the research gap fields to explore future research directions in nighttime visual enhancement techniques. Finally, we discussed various open issues and future directions in the context enhancement based nighttime enhancement research.