Video Acquisition Devices Research Articles

Real-Time Image Acquisition and Stabilization Technology for Dynamic Robotic Dog Loading Video Acquisition Device

Real-Time Image Acquisition and Stabilization Technology for Dynamic Robotic Dog Loading Video Acquisition Device

Image and Video Compression With Neural Networks: A Review

In recent years, the image and video coding technologies have advanced by leaps and bounds. However, due to the popularization of image and video acquisition devices, the growth rate of image and video data is far beyond the improvement of the compression ratio. In particular, it has been widely recognized that there are increasing challenges of pursuing further coding performance improvement within the traditional hybrid coding framework. Deep convolution neural network (CNN) which makes the neural network resurge in recent years and has achieved great success in both artificial intelligent and signal processing fields, also provides a novel and promising solution for image and video compression. In this paper, we provide a systematic, comprehensive and up-to-date review of neural network based image and video compression techniques. The evolution and development of neural network based compression methodologies are introduced for images and video respectively. More specifically, the cutting-edge video coding techniques by leveraging deep learning and HEVC framework are presented and discussed, which promote the state-of-the-art video coding performance substantially. Moreover, the end-to-end image and video coding frameworks based on neural networks are also reviewed, revealing interesting explorations on next generation image and video coding frameworks/standards. The most significant research works on the image and video coding related topics using neural networks are highlighted, and future trends are also envisioned. In particular, the joint compression on semantic and visual information is tentatively explored to formulate high efficiency signal representation structure for both human vision and machine vision, which are the two dominant signal receptor in the age of artificial intelligence.

3D imaging system for respiratory monitoring in pediatric intensive care environment

Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient’s condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. However, most of them are unsuitable for clinical environment or require physical contact with the patient, which limits their efficiency. In this paper, we present a novel system for measuring the breathing pattern based on a computer vision method and contactless design. Our 3D imaging system is specifically designed for pediatric intensive care environment, which distinguishes it from the other imaging methods. Indeed, previous works are mostly limited to the use of conventional video acquisition devices, in addition to not considering the constraints imposed by intensive care environment. The proposed system uses depth information captured by two (Red Green Blue-Depth) RGB-D cameras at different view angles, by considering the intensive care unit constraints. Depth information is then exploited to reconstruct a 3D surface of a patient's torso with high temporal and spatial resolution and large spatial coverage. Our system captures the motion information for the top of the torso surface as well as for its both lateral sides. For each reconstruction, the volume is estimated through a recursive subdivision of the 3D space into cubic unit elements. The volume change is then calculated through a subtraction technique between successive reconstructions. We tested our system in the pediatric intensive care unit of the Sainte-Justine university hospital center, where it was compared to the gold standard method currently used in pediatric intensive care units. The performed experiments showed a very high accuracy and precision of the proposed imaging system in estimating respiratory rate and tidal volume.

Metrics and methods of video quality assessment: a brief review

With rapid development of video acquisition devices and wide applications of video data, more and more requirements are established to use video data query. video quality assessment and improvement become popular and important research issues which attract lots of researchers. The video quality can influence technique application, user experience, and application results. This paper firstly reviews research work on video query based applications. Then, various metrics of video quality assessment are reviewed according to the requirement of reference video, including full-reference metrics, no-reference metrics and reduced-reference metrics. In addition, methods of video quality assessment are reviewed by the features, which include visual features, bitstream-based or packet-based features, data features. Finally, a number of video quality improvement methods are briefly introduced.

The Design of Wireless Video Monitoring System Based on FPGA

The wireless video monitoring system contains the video acquisition device,video transmission device,video storage device and VGA display device.In this paper,we use video acquisition device to collect video siganals in real-time.The analog video signal is transmitted by using wireless technology.The video signal is converted to a digital signal by using the dedicated A/D chip. At the same time ,the YCrCb signals will be converted into RGB signals by the format converting module.Then,the digital RGB signals are converted to analog RGB signals through the D/A,and they are finally displayed on the VGA monitor in real-time.The design mainly uses the wireless transmission technology to transmit analog video signals and uses ADV7181 to decode.The controlling system of FPGA deals with the decoded digital signals which will be transmitted to the D/A and the data finally will display in real time.

Design of Mobile Video Monitoring System Based on Android

This paper introduces a mobile video monitoring system Based on Android. This Mobile video monitoring system consists of video acquisition device, DVR, server, wireless network and mobile monitor client. Hierarchy of RTP/RTCP in the system is discussed in detail, as well as the decoding of the Real-time data streams in the client.

Vision based Fire Detection and Robot Maneuvering Algorithm

based fire detection is potentially a useful technique. With the increase in the number of surveillance cameras being installed, a vision based fire detection capability can be incorporated in existing surveillance systems at relatively low additional cost. Vision based fire detection offers advantages over the traditional methods. It will thus complement the existing devices. Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed system consists of two main parts: fire color modeling and robot navigation. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms in order to rescue indoor appliances. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A fire color model is developed in RGB color space to identify fire pixels. The proposed fire color model is tested with video sequences captured in different illumination condition in indoor ambience. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. In addition; the navigation of the robot is tested through a simulated environment. General Terms

Forest Fire Detection Algorithm Based on Digital Image

A series of computer vision-based fire detection algorithms is proposed in this paper. These algorithms can be used in parallel with conventional fire detection systems to reduce false alarms. They can also be deployed as a stand- alone system to detect fire combined with a video acquisition device. This paper chiefly discussed the segmentation problem. It adopted the L*a*b*, YCbCr color space and k- means clustering algorithm to isolate the forest fire detection. This algorithm is characterized by high stabilization and versatility, improves fire detection in real time and accurate, and has been used in the forest fires detecting. And this performed well in experiments.

Motion-compensated interpolation for face-centered-orthorhombic sampled video sequence

Face-centered orthorhombic (FCO) sampling can be implemented more easily on CMOS image sensors than on other video acquisition devices. The sampling efficiency of FCO is the highest among all three-dimensional (3D) sampling schemes. However, interpolation of FCO-sampled data is inevitable in bridging human perception and machine-vision algorithms. In this letter, the concept of motion compensation is borrowed from deinterlacing, which displays interlaced videos on progressively scanned devices. The combination of motion estimation based on intrafield interpolated frames and motion-compensated interfield interpolation is found to provide the best performance by evaluating different combinations of motion estimation and interpolation.

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision-based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand-alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state-of-the-art fire detection method.

Media objects for user-centered similarity matching

The increase of digital image and video acquisition devices, combined with the growth of the World Wide Web, requires the definition of user-relevant similarity matching methods providing meaningful access to documents searched by users among large amounts of data. The aim of our work is to define media objects for document description suited to images and videos, integrating a user-centered definition of importance for similarity matching. The importance is defined according to criteria and hypotheses, which have been experimentally validated. This leads to a definition of a weighting scheme for media objects (based on objects size, position, and scene homogeneity), which has also been validated with users in a second experiment. This model allows for meaningful similarity matching between document pairs and between users' queries and documents.