Analysis of Real Time Surveillance System on Hadoop Image Processing Interface

Traditional security systems work to avoid crimes as much as possible. Real-time Surveillance gives an opportunity to prevent crimes before they can happen. Implementing security measures are also very time consuming and usually requires human interference. An autonomous security system will make security economically viable and it works quickly. Using facial, object and behavior recognition on the video feed provided by CCTV cameras, various criminal activities can be detected, and authorities will be assisted to take action. Covering a large number of CCTV’s distributed over wide space can generate lots of data and requires tremendous processing power to process this data. Hence, we will use Hadoop’s image processing interface to distribute the processing task over the cloud network, so communication between authorities of various areas is enhanced. In the current time, at almost all locations, the security systems work in a rather passive way. CCTV cameras installed in these system record videos and feed them to a human supervisor. Such a security system is prone to human errors. Quick actions are not possible which are necessary for many conditions to prevent adversary. The entire security works locally and provides with limited cloud capabilities. Such a static system is outdated and itself is under security threat of being misused and hacked. Hence we propose a modern, dynamic system with capabilities to work in the cloud with powerful real-time surveillance and arguably cheaper than the existing system. Footages from multiple CCTV cameras will reach to a local station. These video feed will be provided to a preliminary object recognition algorithms and will undergo the process of culling in the local station. After the initial process of object recognition, the video feed will be divided into a small unit, which comprises multiple images. This images will be mapped to the respective nodes for processing and their results will be reduced to get the final output. The Authors in [1] proposed a scalable video processing system over the Hadoop network. The system uses FFmpeg for video coding and OpenCV for Image processing. They also demonstrate a face tracking system, which grouped multiple images of the same people together. Video feed captured is stored in the Hadoop Distributed file system. The system does not state proper security mechanisms and storing such huge amount of data in the HDFS will not be cost-efficient, The system in [2] used Nvidia CUDA enabled Hadoop clusters to improve server performance by using the parallel processing capability of CUDA cores present in Nvidia GPU’s. They demonstrated an AdaBoost-based face detection algorithm in the Hadoop Network. Although equipping the clusters with Nvidia GPU’s might increase the cost of clusters, CUDA cores potentially provide massive improvements in Image processing jobs. Although we aim to implement the system into existing hardware to minimize the cost. The Authors in [3] used the Hadoop Framework to process astronomical Images. They implemented a scalable image-processing pipeline over Hadoop, which provided for cloud computing of Astronomical Images. They used an existing C++ Library and JNI to use that library in Hadoop for Image processing. Although they achieve success, many optimizations were not made and Hadoop was not Integrated properly with the C++ Library. A survey in [4] describes various security services provided in the Hadoop Framework. Security services, which are necessary for the framework such as Authentication, Access Control, and Integrity, are discussed including what Hadoop provides and what it does not. Hadoop has multiple security flaws which can be exploited to initialize a replay attack or view the files stored in the HDFS node. Hence as per the scholarly, a good Integrity check method and Authorization control method are necessary. The object recognition stated in [5] provides an efficient way of recognizing a 3-Dimensional Object from a 2-Dimensional Image. In his stated methodology, certain features of the object remain constant regardless of the viewing angle. Extracting these features specifically will save a tremendous amount of resources as compared to the older object recognition systems that recreate the entire 3-D objects using Depth Analysis. As depicted in [6], the original eigenfaces fail to accurately classify faces when the data is coming from different angles and light sources like in our problem. Hence, we use the concept of TensorFace. A vector space of different Images trained at multiple angles is applied to N-mode SVD to Multilinear Analysis to recognize faces. Behaviour Recognition can be carried out as stated in [7]. The features will be extracted from the video feed and applied to feature descriptors, model events, and Event/behaviour, models. The output will be mapped from feature space to behavior label space where a classifier will map it as normal or abnormal. The system proposed in [8] states an economic, reliable, efficient and scalable surveillance system where data is stored using P2P concept. It avoids load on a single Data Centre and divides the load into multiple Peer Nodes. It also provides authentication as a module between the Peer Nodes and the directory nodes. The system doesn’t present any method to implement computer vision and integrity check. Proposes an open source Hadoop Video processing Interface integrate C/C++ applications in the Hadoop Framework. It provides R/W interface for developers to store, retrieve and analyze video data from the HDFS. Using the available security in the Hadoop framework for video data can give poor performance and security was not mentioned in the HVPI. TensorFlow, a machine Learning System, stated in [10], provides multiple tools to implement multiple training algorithms and optimizations for multiple devices on a large scale. It uses data flow graphs for computation states and operations that change those states. TensorFlow can work very well with Hadoop Framework to distribute the processing in the existing hardware. To provide real-time recognition various pre-processing is done to improve Hadoop and neural network performance. The entire process can be divided into the following phases:- Video Collection: The video feed coming from the video capture device like CCTV will be converted to the Hip Image Bundle (HIB) object using various tools like Hib Import, info. After that, HIB will undergo preprocessing using a video coder like Culler class and FFmpeg. In this stage, various user-defined conditions like spatial resolution or the criteria for Image metadata can be applied. Filters like a greyscale filter provide improvements for various face detection algorithms. The images surviving the culling phase will undergo the preliminary object detection phase using object detection algorithms like tensor flow or provided by a library like OpenCV. Weapons, Cars, and Humans will be detected in this phase. The collected Image will be mapped to MapReduce programming model using the HibInputFormat class. The individual Images are presented to Mapper as objects derived from the HipiImage abstract class associated with HipiImageHeader. The header will determine the what data to map to the respective data node in the network. Mapping Phase: Images, which are flagged as humans, will be mapped to the facial recognition and behavior recognition algorithms in the respective data nodes. Images recognized as cars will be mapped to object detection. Various algorithms for recognition in the mapping phase can be derived from OpenCV, which also inherently used Nvidia CUDA and OpenCL for increased performance in the recognition. OpenCV provides Java interface and can be directly used with Hadoop. Although a self-developed can be used and if required, will be written in C++ and JNI ( Java Native Interface) can use to integrate with Hadoop. Reduce Phase: Criminal faces will be detected during facial detection since the node with the highest confidence value will be declared as the winner. Stolen cars will also be detected in the similar fashion. Human behavior will classify and detect specific suspicious behavior. Although the above paper only discusses specific applications, the entire architecture is scalable to be implemented in specific environments. The system can find applications in various companies offices, police department and various high-security facilities for real-time computer vision assistance. The system can also be implemented over the existing hardware either as a complement to the existing system or as a substitute to the existing system. Once enough test samples are collected, various optimizations can be used like different neural networks, more suited to the specific applications. Optimizations can also be made to the Java Native Interface (JNI) to improve further performance. Various pre-processing techniques in the video coder can be applied to improve the neural network performance.

Remote sensing project Annotated Bibliography Example | Topics and Well Written Essays - 250 words

Remote sensing project - Annotated Bibliography Example ment and social acceptance of the American people; since an estimated 30000 drones will be expected to be across the US airspace by 2020 (Hiltner, 2013, p 398). Hiltner proposes the advantages and ease of execution of police roles with the integration of the UAss, as highlighted by their manufacturer and mandated by the constitution of the United States. Koppel’s â€Å"Warranting a warrant† discuses the use of the Fourth Amendment in enhancing privacy and the use of modern technological gadget that may intrude the essence of privacy without a warrant. He uses the concept of a search warrant to emphasize the application of the fourth Amendment in providing rights to protection against the violation of private property. The document provides relevance in the use of police drones as a violation of the Fourth Amendment. However, the action is significant in the provision of security in the US. In this case, Koppel concludes that the Fourth Amendment should be reconstructed in a manner that conserves general public interest as well as individual rights. Kyllo vs United States explores the use of legal approaches by law enforces in the aim of attaining peace and security. The use of a device that is not in public utility to conduct private search can be classified under violation of the Fourth Amendment. In this case the use of drones in surveillance activities by the police force can be viewed as an act of trespass which is against the rights of individual privacy. The fact that drones are not silent portrays violation of privacy as people tend to feel they are under surveillance when drones pass by their homes. Katina Michael, MG Michael discusses how modern technology has influenced the invasion of people’s privacy. The use of â€Å"embedded sensors on wearers† is one of the modern technology inventions which are aimed at acquiring information on a person’s nature. The article is relevant to the topic of discussion as it explains the pros and cons of privacy

The Rise of Neoliberism Essay Example | Topics and Well Written Essays - 1000 words

The Rise of Neoliberism - Essay Example The collapse of the Soviet Union was the proverbial straw that broke the forbearance of the economists and social scientists. There arose a dogma that is seldom referred to by its name of neoliberalism, that became increasing popular as a counter-revolution to the communist ideology and the centralised economic system. Neoliberalism, in its basic form, is a movement that encourages a reversion to the economic policies of the 18th and the 19th centuries, and foresees economic liberty and political development as its consequences (Wikipedia 2007). The proponents of this ideology claim it to be more than just an economic and political system; they put forward this counter-revolution as a social and philosophical change (Wikipedia 2007) that will affect all people from all walks of life in all their social endeavors. Neoliberalism aims at providing a freedom in the economic sector through free market and free trade concepts, and a reduced political intervention over the economic sector. It revolves around the privatisation of the public sector, and the transfer of public assets to a select few in the business world. Although neoliberalism aims at promoting liberty, it is of... This ideology encourages a huge rift in the society between the rich and the poor, creating two distinct classes of the people; the working class and the ruling class. In essence, it makes the rich richer and the poor poorer, a signature affect of capitalism, though it claims to be operating on a different note than capitalism.Its basic fundamentals of free market economy are in conjunction with global trade, and the two ideologies intermingle smoothly into each other, that of neoliberalism and globalization. Of late, countries all over the world are under intense pressure to succumb to this model of economics, often referred to as the American Model (Cambridge Journal of Economics 2007), allowing cross-border trade and funds transfer, and subduing the local and preferred economic systems of the affected countries. Neoliberalism, suffice it to say, is an oppressive form of political and economic system that uses force and twisted ideologies to benefit only a handful of ruling parties. It condemns union rights, stating that they come as impediments in the way of economic development. But this brings with itself the oppression of the working class in the form of low wages, under employment and unfair working environments and systems. Although claiming to be a beacon of liberty, it results in non-mobilisation of wealth and property, never letting the working class the right of ownership and governing