Crime Monitoring and Controlling System by Mobile Device

The Closed Circuit Television (CCTV) have been used at very large scale for monitoring, recording and getting popular in whole world. The major goal of Closed Circuit Television system is monitoring or observing crime and tracking the objects. The smart phone Mobile world is also expanding at a rapid scale since the technology was invented. Most of smart phones users live in those countries where usage of CCTV system is very common in life. This project studies a monitoring system for smart phone mobile users based on CCTV system, where information will be sent from mobile phones to server so that CCTV system can work more specifically and accurately by monitoring and tracking objects. A safety assurance approach is proposed, in which a user can inform his location for close observation. If he/she feels like a potential threat. In that case of emergency situation, location, problem and all possible difficulties can be determined in comparatively less time by concern authorities like police as they have already monitoring the situation. DOI: 10.17762/ijritcc2321-8169.15012

Dynamic Reconfiguration in Camera Networks: A Short Survey

There is a clear trend in camera networks towards enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally we discuss the main open research challenges in camera network reconfiguration

Communication and Control in Collaborative UAVs: Recent Advances and Future Trends

The recent progress in unmanned aerial vehicles (UAV) technology has significantly advanced UAV-based applications for military, civil, and commercial domains. Nevertheless, the challenges of establishing high-speed communication links, flexible control strategies, and developing efficient collaborative decision-making algorithms for a swarm of UAVs limit their autonomy, robustness, and reliability. Thus, a growing focus has been witnessed on collaborative communication to allow a swarm of UAVs to coordinate and communicate autonomously for the cooperative completion of tasks in a short time with improved efficiency and reliability. This work presents a comprehensive review of collaborative communication in a multi-UAV system. We thoroughly discuss the characteristics of intelligent UAVs and their communication and control requirements for autonomous collaboration and coordination. Moreover, we review various UAV collaboration tasks, summarize the applications of UAV swarm networks for dense urban environments and present the use case scenarios to highlight the current developments of UAV-based applications in various domains. Finally, we identify several exciting future research direction that needs attention for advancing the research in collaborative UAVs

The AXIOM software layers

AXIOM project aims at developing a heterogeneous computing board (SMP-FPGA).The Software Layers developed at the AXIOM project are explained.OmpSs provides an easy way to execute heterogeneous codes in multiple cores. People and objects will soon share the same digital network for information exchange in a world named as the age of the cyber-physical systems. The general expectation is that people and systems will interact in real-time. This poses pressure onto systems design to support increasing demands on computational power, while keeping a low power envelop. Additionally, modular scaling and easy programmability are also important to ensure these systems to become widespread. The whole set of expectations impose scientific and technological challenges that need to be properly addressed.The AXIOM project (Agile, eXtensible, fast I/O Module) will research new hardware/software architectures for cyber-physical systems to meet such expectations. The technical approach aims at solving fundamental problems to enable easy programmability of heterogeneous multi-core multi-board systems. AXIOM proposes the use of the task-based OmpSs programming model, leveraging low-level communication interfaces provided by the hardware. Modular scalability will be possible thanks to a fast interconnect embedded into each module. To this aim, an innovative ARM and FPGA-based board will be designed, with enhanced capabilities for interfacing with the physical world. Its effectiveness will be demonstrated with key scenarios such as Smart Video-Surveillance and Smart Living/Home (domotics).Peer ReviewedPostprint (author's final draft

Self-organising zooms for decentralised redundancy management in visual sensor networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras’ fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking omnidirectional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator’s preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator’s preferences

Security and the smart city: A systematic review

The implementation of smart technology in cities is often hailed as the solution to many urban challenges such as transportation, waste management, and environmental protection. Issues of security and crime prevention, however, are in many cases neglected. Moreover, when researchers do introduce new smart security technologies, they rarely discuss their implementation or question how new smart city security might affect traditional policing and urban planning processes. This systematic review explores the recent literature concerned with new ‘smart city’ security technologies and aims to investigate to what extent these new interventions correspond with traditional functions of security interventions. Through an extensive literature search we compiled a list of security interventions for smart cities and suggest several changes to the conceptual status quo in the field. Ultimately, we propose three clear categories to categorise security interventions in smart cities: Those interventions that use new sensors but traditional actuators, those that seek to make old systems smart, and those that introduce entirely new functions. These themes are then discussed in detail and the importance of each group of interventions for the overall field of urban security and governance is assessed

Combining Supervisory Control and Data Acquisition (SCADA) with Artificial Intelligence (AI) as a Video Management System

The latest Video management systems (VMS) software relies on CCTV surveillance systems that can monitor a larger number of cameras and sites more efficiently. In this paper, we study the utilization of SCADA to control a network of surveillance IP cameras. Therefore, the video data are acquired from IP cameras, stored and processed, and then transmitted and remotely controlled via SCADA. Such SCADA application will be very useful in VMS in general and in large integrated security networks in particular. In fact, modern VMS are progressively doped with artificial intelligence (AI) and machine learning (ML) algorithms, to improve their performance and detestability in a wide range of control and security applications. In this chapter, we have discussed the utilization of existing SCADA cores, to implement highly efficient VMS systems, with minimum development time. We have shown that such SCADA-based VMS programs can easily incubate AI and deep ML algorithms. We have also shown that the harmonic utilization of neural networks algorithms (NNA) in the software core will lead to an unprecedented performance in terms of motion detection speed and other smart analytics as well as system availability

Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds

Humans can robustly recognize and localize objects by integrating visual and auditory cues. While machines are able to do the same now with images, less work has been done with sounds. This work develops an approach for dense semantic labelling of sound-making objects, purely based on binaural sounds. We propose a novel sensor setup and record a new audio-visual dataset of street scenes with eight professional binaural microphones and a 360 degree camera. The co-existence of visual and audio cues is leveraged for supervision transfer. In particular, we employ a cross-modal distillation framework that consists of a vision `teacher' method and a sound `student' method -- the student method is trained to generate the same results as the teacher method. This way, the auditory system can be trained without using human annotations. We also propose two auxiliary tasks namely, a) a novel task on Spatial Sound Super-resolution to increase the spatial resolution of sounds, and b) dense depth prediction of the scene. We then formulate the three tasks into one end-to-end trainable multi-tasking network aiming to boost the overall performance. Experimental results on the dataset show that 1) our method achieves promising results for semantic prediction and the two auxiliary tasks; and 2) the three tasks are mutually beneficial -- training them together achieves the best performance and 3) the number and orientations of microphones are both important. The data and code will be released to facilitate the research in this new direction.Comment: Project page: https://www.trace.ethz.ch/publications/2020/sound_perception/index.htm