Smart Video Surveillance System Research Articles

Breaking the Limits in Urban Video Monitoring: Massive Crowd Sourced Surveillance over Vehicles

Contemporary urban environments are in prompt need of the means for intelligent decision- making, where a crucial role belongs to smart video surveillance systems. While existing deployments of stationary monitoring cameras already deliver notable societal benefits, the proposed concept of massive video surveillance over connected vehicles that we contribute in this article may further augment these important capabilities. We therefore introduce the envisioned system concept, discuss its implementation, outline the high-level architecture, and identify major data flows, while also offering insights into the corresponding design and deployment aspects. Our conducted case study confirms the potential of the described crowd sourced vehicular system to effectively complement and eventually surpass even the best of today's static video surveillance setups. We expect that our proposal will become of value and integrate seamlessly into the future Internetof- Things landscape, thus enabling a plethora of advanced urban applications.

Real-Time FPGA-Based Object Tracker with Automatic Pan-Tilt Features for Smart Video Surveillance Systems

The design of smart video surveillance systems is an active research field among the computer vision community because of their ability to perform automatic scene analysis by selecting and tracking the objects of interest. In this paper, we present the design and implementation of an FPGA-based standalone working prototype system for real-time tracking of an object of interest in live video streams for such systems. In addition to real-time tracking of the object of interest, the implemented system is also capable of providing purposive automatic camera movement (pan-tilt) in the direction determined by movement of the tracked object. The complete system, including camera interface, DDR2 external memory interface controller, designed object tracking VLSI architecture, camera movement controller and display interface, has been implemented on the Xilinx ML510 (Virtex-5 FX130T) FPGA Board. Our proposed, designed and implemented system robustly tracks the target object present in the scene in real time for standard PAL (720 × 576) resolution color video and automatically controls camera movement in the direction determined by the movement of the tracked object.

A Linked List-Based Algorithm for Blob Detection on Embedded Vision-Based Sensors.

Blob detection is a common task in vision-based applications. Most existing algorithms are aimed at execution on general purpose computers; while very few can be adapted to the computing restrictions present in embedded platforms. This paper focuses on the design of an algorithm capable of real-time blob detection that minimizes system memory consumption. The proposed algorithm detects objects in one image scan; it is based on a linked-list data structure tree used to label blobs depending on their shape and node information. An example application showing the results of a blob detection co-processor has been built on a low-powered field programmable gate array hardware as a step towards developing a smart video surveillance system. The detection method is intended for general purpose application. As such, several test cases focused on character recognition are also examined. The results obtained present a fair trade-off between accuracy and memory requirements; and prove the validity of the proposed approach for real-time implementation on resource-constrained computing platforms.

Directional Prediction CamShift algorithm based on Adaptive Search Pattern for moving object tracking

Moving object tracking is a fundamental task on smart video surveillance systems, because it provides a focus of attention for further investigation. Continuously Adaptive MeanShift (CamShift) algorithm is an adaptation of the MeanShift algorithm for moving objects tracking significantly, and it has been attracting increasing interests in recent years. In this work, a new CamShift approach, Directional Prediction CamShift (DP-CamShift) algorithm, is proposed to improve the tracking accuracy rate. According to the characteristic of the center-based motion vector distribution for the real-world video sequence, this work employs an Adaptive Search Pattern (ASP) to refine the central area search. The proposed approach is more robust because it adapts the optimal search pattern methods for the most adequate direction of the moving target. Since the fast Motion Estimation (ME) method has its own moving direction feature, we can adaptively use the most proper fast ME method to the certain moving object to have the best performance. Furthermore for estimation in large motion situations, the strategy of the DP-CamShift can preserve good performance. For the test video sequences with frame size of 320 × 240, the experimental results indicate that the proposed algorithm can have an accuracy rate of 99 % and achieve 23 frames per second (FPS) processing speed.

An Intelligent Video Surveillance System for Android Smart Phone

Video surveillance technology is playing an important role, and it is widely used in some fields. With the popularity of Android OS, it draws researchers attention to increase the development of video surveillance systems on the platform. This paper presents a smart real-time video surveillance system based on Android smart phone. This system detects moving object by using improved GMM (Gaussian Mixture Mode) algorithm, recognizes invading human with cascade classifier, processes image data with coder & decoder, transmits data over RTP (Real-time Transport Protocol). It also applies some methods to improve the accuracy of moving object detection and recognition, speed up recognition process. The experimental evidences show that it can realize real-time video surveillance and smart alarm.

Gesture Analysis Algorithms

In this paper we use a Kinect camera to monitor extensively human movements and evaluate facial expressions in order to implement a smart video surveillance system in a house inhabited with people with Autism Spectrum Disorders. The main problem here is to select the best approach by testing and analyzing several gesture recognition algorithms.Despite the tests are made on people without Autism Spectrum Disorders, its known that people with Autism Spectrum Disorders usually reveal stereotypic motor behaviors and characteristic reactions to social interactions. In most cases the diagnosis techniques are subjective because they rely on the experience of the parents with the children and the time spent with the doctor is very little in order to be made a complete examination to the children's natural behavior [1][2][3].

Collaborative occupancy reasoning in visual sensor network for scalable smart video surveillance

Video surveillance is a popular consumer application that is used for various purposes such as public safety, facilities surveillance, and traffic monitoring. In a general video surveillance system, video streams from cameras are sent to a control center and operators monitor the videos. But human operator monitoring of the views every moment of every day is almost impossible; so, smart surveillance systems are required, systems that are capable of automated scene analysis. There are a number of studies to enable smart video surveillance in a multi-camera network. Most of the studies, however, treat central processing approaches in which a scene analysis is processed inside a central server domain once all available information has been collected in the server. Such approaches require tremendous efforts in building the system and, moreover, limit the scalability. To accomplish scalable smart video surveillance, an inference framework in visual sensor networks is necessary, one in which autonomous scene analysis is performed via distributed and collaborative processing among camera nodes without necessity for a high performance server. In this paper, we propose a collaborative inference framework for visual sensor networks and an efficient occupancy reasoning algorithm that is essential in smart video surveillance based on the framework. We estimate the existence probabilities for every camera and combine them using the work-tree architecture in a distributed and collaborative manner. We aim for practical smart video surveillance systems.

SVSS: Intelligent video surveillance system for aircraft

Safety and security are the most discussed topics in the aviation field. The latest security initiatives in the field of aviation propose [1I] the aircraft carriers to implement video surveillance within the aircraft at strategic locations. The current proposals allow the video surveillance data to be stored within the aircraft and monitored by one of the flight crew. The monitoring crew will be responsible for identifying the anomaly within the aircraft and take necessary preventive actions. With the introduction of additional technology within the aircraft, mere human perception may not be sufficient to make a decision. In this research work, the authors explore the possibility of implementing a smart video surveillance system (SVSS) within the aircraft that is tuned toward detecting the behavioral anomaly within the aircraft. The SVSS will generate security triggers when it detects an anomaly within the aircraft. These triggers could be combined with other triggers generated by different aircraft components (possible alarms from the flight crew, data traffic anomaly, or alarm generated by one of the avionics components) to provide a better understanding of the situation to the monitoring crew.

Smart video surveillance for airborne platforms

This paper describes real-time computer vision algorithms for detection, identification, and tracking of moving targets in video streams generated by a moving airborne platform. Moving platforms cause instabilities in image acquisition due to factors such as disturbances and the ego-motion of the camera that distorts the actual motion of the moving targets. When the camera is mounted on a moving observer, the entire scene (background and targets) appears to be moving and the actual motion of the targets must be separated from the background motion. The motion of the airborne platform is modeled as affine transformation and its parameters are estimated using corresponding feature sets in consecutive images. After motion is compensated, the platform is considered as stationary and moving targets are detected accordingly. A number of tracking algorithms including particle filters, mean-shift, and connected component were implemented and compared. A cascaded boosted classifier with Haar wavelet feature extraction for moving target classification was developed and integrated with the recognition system that uses joint-feature spatial distribution. The integrated smart video surveillance system has been successfully tested using the Vivid Datasets provided by the Air Force Research Laboratory. The experimental results show that system can operate in real time and successfully detect, track, and identify multiple targets in the presence of partial occlusion.

Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking

Situation awareness is the key to security. Awareness requires information that spans multiple scales of space and time. Smart video surveillance systems are capable of enhancing situational awareness across multiple scales of space and time. However, at the present time, the component technologies are evolving in isolation. To provide comprehensive, nonintrusive situation awareness, it is imperative to address the challenge of multiscale, spatiotemporal tracking. This article explores the concepts of multiscale spatiotemporal tracking through the use of real-time video analysis, active cameras, multiple object models, and long-term pattern analysis to provide comprehensive situation awareness.