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Multimodal Video Analysis on Self-Powered Resource-Limited Wireless Smart Camera
"... Abstract—Surveillance is one of the most promising applica-tions for wireless sensor networks, stimulated by a confluence of simultaneous advances in key disciplines: computer vision, image sensors, embedded computing, energy harvesting, and sensor networks. However, computer vision typically requir ..."
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Abstract—Surveillance is one of the most promising applica-tions for wireless sensor networks, stimulated by a confluence of simultaneous advances in key disciplines: computer vision, image sensors, embedded computing, energy harvesting, and sensor networks. However, computer vision typically requires notable amounts of computing performance, a considerable memory foot-print and high power consumption. Thus, wireless smart cameras pose a challenge to current hardware capabilities in terms of low-power consumption and high imaging performance. For this reason, wireless surveillance systems still require considerable amount of research in different areas such as mote architec-tures, video processing algorithms, power management, energy harvesting and distributed engine. In this paper, we introduce a multimodal wireless smart camera equipped with a pyroelectric infrared sensor and solar energy harvester. The aim of this work is to achieve the following goals: 1) combining local processing, low power hardware design, power management and energy harvesting to develop a low-power, low-cost, power-aware, and self-sustainable wireless video sensor node for video processing on board; 2) develop an energy efficient smart camera with high accuracy abandoned/removed object detection capability. The efficiency of our approach is demonstrated by experimental results in terms of power consumption and video processing accuracy as well as in terms of self-sustainability. Finally, simulation results show how perpetual work can be achieved in an outdoor scenario within a typical video surveillance application dealing with aban-doned/removed object detection. Index Terms—Embedded smart camera, energy efficient, in-frared sensor, multimodal video surveillance system, wireless sensor network. I.
Adaptive background modeling in multicamera system for real-time object detection
"... Adaptive background modeling in multicamera system for real-time object detection ..."
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Adaptive background modeling in multicamera system for real-time object detection
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 1 Foreground-Background Separation From Video Clips via Motion-Assisted Matrix Restoration
"... Abstract—Separation of video clips into foreground and back-ground components is a useful and important technique, making recognition, classification and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation in ..."
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Abstract—Separation of video clips into foreground and back-ground components is a useful and important technique, making recognition, classification and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground-background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foreground-background separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly-moving objects and camouflages. In addition, we extend our model to a robust MAMR model (R-MAMR) against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos. Index Terms—Background segmentation/subtraction, motion detection, optical flow, matrix restoration, video surveillance.
Foreground–Background Separation From Video Clips via Motion-Assisted Matrix Restoration
"... Abstract — Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification, and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground–background separation ..."
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Abstract — Separation of video clips into foreground and background components is a useful and important technique, making recognition, classification, and scene analysis more efficient. In this paper, we propose a motion-assisted matrix restoration (MAMR) model for foreground–background separation in video clips. In the proposed MAMR model, the backgrounds across frames are modeled by a low-rank matrix, while the foreground objects are modeled by a sparse matrix. To facilitate efficient foreground–background separation, a dense motion field is estimated for each frame, and mapped into a weighting matrix which indicates the likelihood that each pixel belongs to the background. Anchor frames are selected in the dense motion estimation to overcome the difficulty of detecting slowly moving objects and camouflages. In addition, we extend our model to a robust MAMR model against noise for practical applications. Evaluations on challenging datasets demonstrate that our method outperforms many other state-of-the-art methods, and is versatile for a wide range of surveillance videos. Index Terms — Background segmentation/subtraction, matrix restoration, motion detection, optical flow, video surveillance.
Lane Detection-based Camera Pose Estimation
"... Abstract: When a camera installed on a vehicle is used, estimation of the camera pose including tilt, roll, and pan angle with respect to the world coordinate system is important to associate camera coordinates with world coordinates. Previous approaches using huge calibration patterns have the disa ..."
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Abstract: When a camera installed on a vehicle is used, estimation of the camera pose including tilt, roll, and pan angle with respect to the world coordinate system is important to associate camera coordinates with world coordinates. Previous approaches using huge calibration patterns have the disadvantage that the calibration patterns are costly to make and install. And, previous approaches exploiting multiple vanishing points detected in a single image are not suitable for automotive applications as a scene where multiple vanishing points can be captured by a front camera is hard to find in our daily environment. This paper proposes a camera pose estimation method. It collects multiple images of lane markings while changing the horizontal angle with respect to the markings. One vanishing point, the cross point of the left and right lane marking, is detected in each image, and vanishing line is estimated based on the detected vanishing points. Finally, camera pose is estimated from the vanishing line. The proposed method is based on the fact that planar motion does not change the vanishing line of the plane and the normal vector of the plane can be estimated by the vanishing line. Experiments with large and small tilt and roll angle show that the proposed method outputs accurate estimation results respectively. It is verified by checking the lane markings are up right in the bird’s eye view image when the pan angle is compensated.
Human Body Pose Recognition,Appearance Based,Motion-Based and Hybrid methods.
"... Abstract- “Intelligent surveillance system for human detection ” focuses on video level processing techniques to identify human from video. Surveillance System have become increasingly popular in the globalization process. Intelligent Video Surveillance system based on image recognition is widely us ..."
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Abstract- “Intelligent surveillance system for human detection ” focuses on video level processing techniques to identify human from video. Surveillance System have become increasingly popular in the globalization process. Intelligent Video Surveillance system based on image recognition is widely used to effectively help to provide security, safety and prevents many crimes. Due to the high Complexity in techniques such as real-time processing and image contents analysis/understanding. However detecting humans in images and videos still challenging task owing to their variable appearance caused by variety of clothes shadows, articulation and illumination situations, and unpredictable poses that they can adopt. In this paper there is a brief survey of different object detection techniques, as well as Human Detection techniques like fuzzy logic, Single Gaussian model, Mixture of Gaussian model(MOG), Background Subtraction technique,
