Abstract: Security of the smart home in terms of behavior activity pattern recognition is a totally dissimilar and unique issue as compared to the security issues of other scenarios. Sensor devices (low capacity and high capacity) interact and negotiate each other by detecting the daily behavior activity of individuals to execute common tasks. Once a device (e.g., surveillance camera, smart phone and light detection sensor etc.) is compromised, an adversary can then get access to a specific device and can damage daily behavior activity by altering the data and commands. In this scenario, a group of common instruction processes may get involved to generate deadlock. Therefore, an effective suitable security solution is required for smart home architecture. This paper proposes seamless distributed Scheme which fortifies low computational wireless devices for secure communication. Proposed scheme is based on lightweight key-session process to upheld cryptic-link for trajectory by recognizing of individual’s behavior activities pattern. Every device and service provider unit (low capacity sensors (LCS) and high capacity sensors (HCS)) uses an authentication token and originates a secure trajectory connection in network. Analysis of experiments is revealed that proposed scheme strengthens the devices against device seizure attack by recognizing daily behavior activities, minimum utilization memory space of LCS and avoids network from deadlock. Additionally, the results of a comparison with other schemes indicate that scheme manages efficiency in term of computation and communication.
Abstract: In this paper, a design of H.263 based wireless video
transceiver is presented for wireless camera system. It uses standard
WIFI transceiver and the covering area is up to 100m. Furthermore the
standard H.263 video encoding technique is used for video
compression since wireless video transmitter is unable to transmit high
capacity raw data in real time and the implemented system is capable
of streaming at speed of less than 1Mbps using NTSC 720x480 video.
Abstract: Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).Â
Abstract: In recent years, the number of the cases of information
leaks is increasing. Companies and Research Institutions make various
actions against information thefts and security accidents. One of the
actions is adoption of the crime prevention system, including the
monitoring system by surveillance cameras. In order to solve
difficulties of multiple cameras monitoring, we develop the automatic
human tracking system using mobile agents through multiple
surveillance cameras to track target persons. In this paper, we develop
the monitor which confirms mobile agents tracing target persons, and
the simulator of video picture analysis to construct the tracking
algorithm.
Abstract: Mixed-traffic (e.g., pedestrians, bicycles, and vehicles)
data at an intersection is one of the essential factors for intersection
design and traffic control. However, some data such as pedestrian
volume cannot be directly collected by common detectors (e.g.
inductive loop, sonar and microwave sensors). In this paper, a video
based detection algorithm is proposed for mixed-traffic data collection
at intersections using surveillance cameras. The algorithm is derived
from Gaussian Mixture Model (GMM), and uses a mergence time
adjustment scheme to improve the traditional algorithm. Real-world
video data were selected to test the algorithm. The results show that
the proposed algorithm has the faster processing speed and more
accuracy than the traditional algorithm. This indicates that the
improved algorithm can be applied to detect mixed-traffic at
signalized intersection, even when conflicts occur.
Abstract: In this paper, we propose the robust water level detection method based on the accumulated histogram under small changed image which is acquired from water level surveillance camera. In general surveillance system, this is detecting and recognizing invasion from searching area which is in big change on the sequential images. However, in case of a water level detection system, these general surveillance techniques are not suitable due to small change on the water surface. Therefore the algorithm introduces the accumulated histogram which is emphasizing change of water surface in sequential images. Accumulated histogram is based on the current image frame. The histogram is cumulating differences between previous images and current image. But, these differences are also appeared in the land region. The band pass filter is able to remove noises in the accumulated histogram Finally, this algorithm clearly separates water and land regions. After these works, the algorithm converts from the water level value on the image space to the real water level on the real space using calibration table. The detected water level is sent to the host computer with current image. To evaluate the proposed algorithm, we use test images from various situations.