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Criminals often resort to camera tampering to prevent capture of their actions. Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering is generally done by obstruc...
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Criminals often resort to camera tampering to prevent capture of their actions. Real-time automated detection of video camera tampering cases is important for timely warning of the operators. Tampering is generally done by obstructing the camera view by a foreign object, displacing the camera and changing the focus of the camera lens. In automated camera tamper detection systems, low false alarm rates are important as reliability of these systems is compromised by unnecessary alarms and consequently the operators start ignoring the warnings. We propose adaptive algorithms to detect and identify such cases with low false alarms rates in typical surveillance scenarios where there is significant activity in the scene.
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Depth sensing devices have created various new applications in scientific and commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing Device) cameras. Most of these applications require the depth cameras to ...
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Depth sensing devices have created various new applications in scientific and commercial research with the advent of Microsoft Kinect and PMD (Photon Mixing Device) cameras. Most of these applications require the depth cameras to be pre-calibrated. However, traditional calibration methods using a checkerboard do not work very well for depth cameras due to the low image resolution. In this paper, we propose a depth calibration scheme which excels in estimating camera calibration parameters when only a handful of corners and calibration images are available. We exploit the noise properties of PMD devices to denoise depth measurements and perform camera calibration using the denoised depth as additional set of measurements. Our synthetic and real experiments show that our depth denoising and depth based calibration scheme provides significantly better results than traditional calibration methods.
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Camera calibration is one of the basic problems concerning intelligent video analysis in networks of multiple cameras with changeable pan and tilt (PT). Traditional calibration methods give satisfactory results, but are human labo...
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Camera calibration is one of the basic problems concerning intelligent video analysis in networks of multiple cameras with changeable pan and tilt (PT). Traditional calibration methods give satisfactory results, but are human labour intensive. In this paper we introduce a method of camera calibration and navigation based on continuous tracking, which requires minimal human involvement. After the initial precalibration, it allows the camera pose to be calculated recursively in real time on the basis of the current and previous camera images and the previous pose. The method is suitable if multiple coplanar points are shared between views from neighbouring cameras, which is often the case in the video surveillance systems.
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Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing l...
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Detection of camera anomaly and tampering have attracted increasing interest in video surveillance for real-time alert of camera malfunction. However, the anomaly detection for traffic cameras monitoring vehicles and recognizing license plates has not been formally studied and it cannot be solved by existing methods. In this paper, we propose a camera anomaly detection method for traffic scene that has distinct characteristics of dynamics due to traffic flow and traffic crowd, compared with normal surveillance scene. Image quality used as low-level features are measured by no-referenced metrics. Image dynamics used as mid-level features are computed by histogram distribution of optical flow. A two-stage classifier for the detection of anomaly is devised by the modeling of image quality and video dynamics with probabilistic state transition. The proposed approach is robust to many challenging issues in urban surveillance scenarios and has very low false alarm rate. Experiments are conducted on real-world videos recorded in traffic scene including the situations of high traffic flow and severe crowding. Our test results demonstrate that the proposed method is superior to previous methods on both precision rate and false alarm rate for the anomaly detection of traffic cameras.
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Pan-tilt-zoom (PTZ) cameras are frequently used in surveillance applications as they can observe a much larger region ofthe environment than a fixed-lens camera while still providing high-resolution imagery. The pan, tilt, and zoo...
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Pan-tilt-zoom (PTZ) cameras are frequently used in surveillance applications as they can observe a much larger region ofthe environment than a fixed-lens camera while still providing high-resolution imagery. The pan, tilt, and zoomparameters of a single camera may be simultaneously controlled by online users as well as automated surveillanceapplications. To accurately register autonomously tracked objects to a world model, the surveillance system requiresaccurate knowledge of camera parameters. Due to imprecision in the PTZ mechanism, these parameters cannot beobtained from PTZ control commands but must be calculated directly from camera imagery. This paper describes theefforts undertaken to implement a real-time calibration system for a stationary PTZ camera. The approach continuouslytracks distinctive image feature points from frame to frame, and from these correspondences, robustly calculates thehomography transformation between frames. Camera internal parameters are then calculated from these homographies.The calculations are performed by a self contained program that continually monitors images collected by the camera asit performs pan, tilt, and zoom operations. The accuracy of the calculated calibration parameters are compared to groundtruth data. Problems encountered include inaccuracies in large orientation changes and long algorithm execution time.
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摘要 :
Pan-tilt-zoom (PTZ) cameras are frequently used in surveillance applications as they can observe a much larger region of the environment than a fixed-lens camera while still providing high-resolution imagery. The pan, tilt, and zo...
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Pan-tilt-zoom (PTZ) cameras are frequently used in surveillance applications as they can observe a much larger region of the environment than a fixed-lens camera while still providing high-resolution imagery. The pan, tilt, and zoom parameters of a single camera may be simultaneously controlled by online users as well as automated surveillance applications. To accurately register autonomously tracked objects to a world model, the surveillance system requires accurate knowledge of camera parameters. Due to imprecision in the PTZ mechanism, these parameters cannot be obtained from PTZ control commands but must be calculated directly from camera imagery. This paper describes the efforts undertaken to implement a real-time calibration system for a stationary PTZ camera. The approach continuously tracks distinctive image feature points from frame to frame, and from these correspondences, robustly calculates the homography transformation between frames. Camera internal parameters are then calculated from these homographies. The calculations are performed by a self contained program that continually monitors images collected by the camera as it performs pan, tilt, and zoom operations. The accuracy of the calculated calibration parameters are compared to ground truth data. Problems encountered include inaccuracies in large orientation changes and long algorithm execution time.
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Video surveillance is very common for security monitoring of premises and sensitive installations. Criminals tamper the surveillance camera settings so that their (criminal) activities in the scene are not recorded properly, there...
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Video surveillance is very common for security monitoring of premises and sensitive installations. Criminals tamper the surveillance camera settings so that their (criminal) activities in the scene are not recorded properly, thereby making the captured video frames useless. Various camera tampering/sabotage include - changing the normal view of the camera by turning the camera away from the scene, obstructing the camera lens by placing some objects in front of the camera or spraying paint on it and defocusing the camera lens by changing the camera focus settings, spraying water or some viscous fluid on it. Manual monitoring of the surveillance systems have many limitations - human fatigue, lack of continuous monitoring, etc. Hence, real-time automated analysis and detection of suspicious events have gained importance. In this paper, we propose an efficient algorithm for camera tamper detection based on background modeling, edge details, foreground object size and its movement. In our testing or experimental setup, the results are encouraging with high precision and low false alarm rate. As the proposed method can process 320 × 240 resolution videos at 60 - 70 frames/sec, it can be implemented for real-time applications.
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In this paper a model for active cameras that considers complex camera dynamics and lens distortion is presented. This model is particularly suited for real-time applications, thanks to the low computational load required when the...
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In this paper a model for active cameras that considers complex camera dynamics and lens distortion is presented. This model is particularly suited for real-time applications, thanks to the low computational load required when the active camera is moved. In addition, a simple technique for interpolating calibration parameters is described, resulting in very accurate calibration over the full range of focal lengths. The proposed system can be employed to enhance the patrolling activity performed by a network of active cameras that supervise large areas. Experiments are also presented, showing the improvement provided over traditional pin-hole camera models.
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We develop a model to study how to place still and/or rotary cameras in some locations around the museum. The goal is to determine the optimal camera locations and the smallest number of the cameras so as to provide the best possi...
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We develop a model to study how to place still and/or rotary cameras in some locations around the museum. The goal is to determine the optimal camera locations and the smallest number of the cameras so as to provide the best possible coverage during the night and be easy to monitor particular items. We model the process how to select every optimal camera location and the kind of camera. Subject to the given constraints, our algorithm outputs the optimal project for every given museum. We apply our algorithm to a case study of a real museum in China in order to test the practicability of the model. Finally, we examine the sensitivity of optimal camera locations and the number of cameras to changes in the unit length of zoning.
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摘要 :
We develop a model to study how to place still and/or rotary cameras in some locations around the museum. The goal is to determine the optimal camera locations and the smallest number of the cameras so as to provide the best possi...
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We develop a model to study how to place still and/or rotary cameras in some locations around the museum. The goal is to determine the optimal camera locations and the smallest number of the cameras so as to provide the best possible coverage during the night and be easy to monitor particular items. We model the process how to select every optimal camera location and the kind of camera. Subject to the given constraints, our algorithm outputs the optimal project for every given museum. We apply our algorithm to a case study of a real museum in China in order to test the practicability of the model. Finally, we examine the sensitivity of optimal camera locations and the number of cameras to changes in the unit length of zoning.
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