Smart camera

About: Smart camera is a research topic. Over the lifetime, 5571 publications have been published within this topic receiving 93054 citations. The topic is also known as: intelligent camera.

Portable digital video camera configured for remote image acquisition control and viewing

Abstract: A wearable digital video camera (10) is equipped with wireless connection protocol and global navigation and location positioning system technology to provide remote image acquisition control and viewing. The Bluetooth® packet-based open wireless technology standard protocol (400) is preferred for use in providing control signals or streaming data to the digital video camera and for accessing image content stored on or streaming from the digital video camera. The GPS technology (402) is preferred for use in tracking of the location of the digital video camera as it records image information. A rotating mount (300) with a locking member (330) on the camera housing (22) allows adjustment of the pointing angle of the wearable digital video camera when it is attached to a mounting surface.

Means and method of robot control relative to an arbitrary surface using camera-space manipulation

Abstract: A system is presented that creates vision-based, three-dimensional control of a multiple-degree-of-freedom dexterous robot, without special calibration of the vision system, the robot, or any of the constituent parts of the system, and that allows high-level human supervision or direction of the robot. The human operator uses a graphical user interface (GUI) to point and click on an image of the surface of the object with which the robot is to interact. Directed at this surface is the stationary selection camera, which provides the image for the GUI, and at least one other camera. A laser pointer is panned and tilted so as to create, in each participating camera space, targets associated with surface junctures that the user has selected in the selection camera. Camera-space manipulation is used to control the internal degrees of freedom of the robot such that selected points on the robot end member move relative to selected surface points in a way that is consistent with the desired robot operation. As per the requirement of camera-space manipulation, the end member must have features, or “cues”, with known location relative to the controlled end-member points, that can be located in the images or camera spaces of participant cameras. The system is extended to simultaneously control tool orientation relative to the surface normal and/or relative to user-selected directions tangent to the surface. The system is extended in various ways to allow for additional versatility of application.

Smart surveillance camera systems and methods

Abstract: Various techniques are disclosed for smart surveillance camera systems and methods using thermal imaging to intelligently control illumination and monitoring of a surveillance scene. For example, a smart camera system may include a thermal imager, an IR illuminator, a visible light illuminator, a visible/near IR (NIR) light camera, and a processor. The camera system may capture thermal images of the scene using the thermal imager, and analyze the thermal images to detect a presence and an attribute of an object in the scene. In response to the detection, various light sources may be selectively operated to illuminate the object only when needed or desired, with a suitable type of light source, with a suitable beam angle and width, or in otherwise desirable manner. The visible/NIR light camera may also be selectively operated based on the detection to capture or record surveillance images containing objects of interest.

Image sensing control method and apparatus, image transmission control method, apparatus, and system, and storage means storing program that implements the method

Abstract: This invention relates to an image sensing control method and apparatus, and an image transmission control method, apparatus, and system, in which a camera such as a video camera is connected, the operation of the camera is controlled in accordance with a control command sent from a client via a network, and an image signal sensed by the camera is transmitted to the client via the network. Upon reception of a control command of the pan and tilt angles, zoom value, or the like of the camera from the client, the position control of the camera is done in accordance with the command, and transmission of the image signal sensed by the camera is suspended until the instructed operation is complete. Alternatively, upon reception of such control command, the image signal sensed by the camera is compressed at a high compression ratio, and the compressed image signal is transmitted. On the client device, received image signal is directly displayed without buffering during the control operation, thus allowing real-time image display during the position control of the camera.

Distributed localization of networked cameras

Abstract: Camera networks are perhaps the most common type of sensor network and are deployed in a variety of real-world applications including surveillance, intelligent environments and scientific remote monitoring. A key problem in deploying a network of cameras is calibration, i.e., determining the location and orientation of each sensor so that observations in an image can be mapped to locations in the real world. This paper proposes a fully distributed approach for camera network calibration. The cameras collaborate to track an object that moves through the environment and reason probabilistically about which camera poses are consistent with the observed images. This reasoning employs sophisticated techniques for handling the difficult nonlinearities imposed by projective transformations, as well as the dense correlations that arise between distant cameras. Our method requires minimal overlap of the cameras' fields of view and makes very few assumptions about the motion of the object. In contrast to existing approaches, which are centralized, our distributed algorithm scales easily to very large camera networks. We evaluate the system on a real camera network with 25 nodes as well as simulated camera networks of up to 50 cameras and demonstrate that our approach performs well even when communication is lossy.