Multiple Video Cameras Research Articles

Distortion‐free fusion of multiple video camera images using EPI analysis

AbstractThis paper presents a method that uses an Epipolar Plane Image (EPI) to calibrate multiple video cameras and obtain distortion‐free fusion of these images. Generally speaking, the optical center of a camera exists inside its body making it physically difficult to focus these images at one point in multiple cameras. When fusing these types of camera images, distortion will occur unless three‐dimensional shape information is used. In this paper we perform distortion‐free image fusion by bringing these optical centers into agreement in a spatiotemporal field based on conditions in which multiple video cameras are installed on a movable object. The agreement points of the optical centers are automatically calculated by means of EPI matching. Because this process utilizes time‐variant redundancy of each camera image, a stable corresponding relationship is obtained even when there is no spatial overlap of the photographic range making it possible to install cameras with comparative freedom. In addition, because it is possible to ensure sufficient quality without using special external synchronization devices or position measurement devices, this method is also suitable for acquiring textures of large‐scale real scenes, such as urban spaces. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(11): 85–98, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20396

Surface Capture for Performance-Based Animation

Creating realistic animated models of people is a central task in digital content production. Traditionally, highly skilled artists and animators construct shape and appearance models for digital character. They then define the character's motion at each time frame or specific key-frames in a motion sequence to create a digital performance. Increasingly, producers are using motion capture technology to record animations from an actor's performance. This technology reduces animation production time and captures natural movements to create a more believable production. However, motion capture requires the use of specialist suits and markers and only records skeletal motion. It lacks the detailed secondary surface dynamics of cloth and hair that provide the visual realism of a live performance. Over the last decade, we have investigated studio capture technology with the objective of creating models of real people that accurately reflect the time-varying shape and appearance of the whole body with clothing. Surface capture is a fully automated system for capturing a human's shape and appearance as well as motion from multiple video cameras to create highly realistic animated content from an actor's performance in full wardrobe. Our system solves two key problems in performance capture: scene capture from a limited number of camera views and efficient scene representation for visualization

Construction Method of Drift-Free Omni Images by Integration of Multiple Video Cameras

Construction Method of Drift-Free Omni Images by Integration of Multiple Video Cameras

Image-based spatio-temporal modeling and view interpolation of dynamic events

We present an approach for modeling and rendering a dynamic, real-world event from an arbitrary viewpoint, and at any time, using images captured from multiple video cameras. The event is modeled as a nonrigidly varying dynamic scene, captured by many images from different viewpoints, at discrete times. First, the spatio-temporal geometric properties (shape and instantaneous motion) are computed. The view synthesis problem is then solved using a reverse mapping algorithm, ray-casting across space and time, to compute a novel image from any viewpoint in the 4D space of position and time. Results are shown on real-world events captured in the CMU 3D Room, by creating synthetic renderings of the event from novel, arbitrary positions in space and time. Multiple such recreated renderings can be put together to create retimed fly-by movies of the event, with the resulting visual experience richer than that of a regular video clip, or switching between images from multiple cameras.

Video Security for Ambient Intelligence

Moving toward the implementation of the intelligent building idea in the framework of ambient intelligence, a video security application for people detection, tracking, and counting in indoor environments is presented in this paper. In addition to security purposes, the system may be employed to estimate the number of accesses in public buildings, as well as the preferred followed routes. Computer vision techniques are used to analyze and process video streams acquired from multiple video cameras. Image segmentation is performed to detect moving regions and to calculate the number of people in the scene. Testing was performed on indoor video sequences with different illumination conditions.

Human movement capture and analysis in intelligent environments

Investigators from multiple disciplines and applications areas are interested in Human body modeling, tracking, and synthesis related topics. These topics offer fertile ground for challenging research problems aswell as potential for awide range of applications. Our own interest and involvement in human modeling, analysis, and synthesis comes froma very specific need: that of developing “intelligent” environments or spaces [1–3]. An intelligent environment automatically derives and dynamically maintains an awareness of its composition as well as events and activities occurring within.Moreover, these spaces should be responsive to specific events and triggers. Such spaces need not be limited to rooms in buildings, but extend to outdoor environments [4] and any other spaces that humans occupy such as a performance on a stage or an automobile on a highway [5]. The spaces are monitored by multiple audio and video sensors, which can be unobtrusively embedded in the infrastructure. To avoid intrusion on the normal human activities in the space, all sensors, processors, and communication devices should remain “invisible” in the infrastructure. The system should also support natural and flexible interactions among the participants without specialized or encumbering devices. In a conference room environment, multiple video cameras and microphones may be embedded in walls and furniture. Video and audio signals are analyzed in real time for a wide range of low-level tasks, including person identification, localization and tracking, and gesture and voice recognition [6]. Combining the analysis tasks with human face and body synthesis enables efficient interactions with remote observers, effectively merging disjoint spaces into a single intelligent environment. We are currently embedding distributed video networks in rooms, laboratories, museums, and even outdoor public spaces in support of experimental research in this domain. This involves the development of new frameworks, architectures, and algorithms for audio and video processing as well as for the control of various functions associated with proper execution of a transaction within such intelligent spaces. These test beds are also helping to identify novel applications of such

Intraguild predation by aphidophagous predators on parasitised aphids: the use of multiple video cameras

AbstractA video technique that allows simultaneous behavioural observations of several experimental replicates under field and laboratory conditions is described. The technique was used to analyse predation risk of parasitised aphids in a sugar beet field. The images of 16 black and white video cameras were recorded by a video multiplexer in combination with a time‐lapse video recorder. Each camera was weather protected and equipped with a single infrared diode to allow observations during night times. Single leaves carrying aphid mummies only or mummies and unparasitised aphids were monitored. All colonies were exposed to predation and parasitation by the community of natural enemies in the field. Colonies with mummies and unparasitised aphids were visited significantly more often by predators than those without additional aphids. Predators also stayed significantly longer in patches with unparasitised aphids. Although an equal proportion of aphid mummies were destroyed in both treatments, the video analysis showed differences in predator species spectrum between treatments. In patches with aphids, coccinellid and hemipteran predators preyed on mummies, while in patches with only mummies, chrysopids accounted for most of the damage. The decrease in parasitoid survival could be attributed to the increasing number of predator visits in aphid patches and to a lesser extent to the decreasing number of unparasitised aphids (alternative prey). Parasitoid survival in colonies without alternative prey was correlated with the number of predator visits and the time predators spent on a leaf.Continuous video observations gave additional behavioural information for the interpretation of field data. Other prospective research fields for the use of the multi video camera technique are outlined and general advantages and disadvantages are discussed.

Deformable Shape Recovery by Factorization Based on a Spatiotemporal Measurement Matrix

An efficient technique based on factorization is presented for recovering the 3-D shape of deformable objects. The original factorization solely recovers the 3-D shape of rigid objects. The present technique applies it to shape recovery of deformable objects. In the proposed technique, multiple video cameras at fixed locations take video images of an object. The obtained image sequences are analyzed to yield measurement matrices at respective sample times and they are merged into a single matrix called a spatiotemporal measurement matrix. Factorization is applied to the matrix once and this results in the entire 3-D shape recovery during observation in a single stage. In the performed experiment, some human motions and the inflation/deflation process of a toy balloon successfully recovered their 3-D shape. Advantages of the present technique are that calibration is not necessary with the employed multiple video cameras, that the entire deformation is recovered by single application of the factorization, which eliminates the alignment of recovered shape in the time axis, that the recovered shape contains less recovery errors because of averaging effect by the employment of the proposed matrix, and that the technique is applicable to any object on which feature points can be specified. Various applications related to human motions recovery are expected.

Applications of Video Mixing and Digital Overlay to Neuroethology

Neuroethological experiments often require video images of animal behavior and recordings of physiological data to be acquired simultaneously, synchronized with each other, stored, and analyzed together. The use of inexpensive multimedia computers offers new possibilities for mixing video images, analog voltages, and computer data, storing these combined signals to videotape, and extracting quantitative data for analysis. In this paper, we summarize methods for mixing images from multiple video cameras and a Macintosh computer display to facilitate manipulation of data generated during our neurophysiological and behavioral research. These technologies enhance accuracy, speed, and flexibility during experiments, and facilitate selecting and extracting quantitative data from the videotape for further analysis. Three applications are presented: (A) we used an analog video mixer to synchronize neurophysiological recordings with ongoing behaviors of freely moving rats; (B) we used a chroma keyed digital overlay to generate positional data for the rat's face during drinking behavior; and (C) we combined a computer model of a rat's head and whiskers with videos of exploratory behaviors to better track and quantify movements in three dimensions. Although the applications described here are specific to our neuroethological work, these methods will be useful to anyone wishing to combine the signals from multiple video sources into a single image or to extract series of positional or movement data from video frames without frame grabbing.