This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications...This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.展开更多
This paper presents a real-time, dynamic system that uses high resolution gimbals and motorized lenses with position encoders on their zoom and focus elements to “recalibrate” the system as needed to track a target....This paper presents a real-time, dynamic system that uses high resolution gimbals and motorized lenses with position encoders on their zoom and focus elements to “recalibrate” the system as needed to track a target. Systems that initially calibrate for a mapping between pixels of a wide field of view (FOV) master camera and the pan-tilt (PT) settings of a steerable narrow FOV slave camera assume that the target is travelling on a plane. As the target travels through the FOV of the master camera, the slave cameras PT settings are then adjusted to keep the target centered within its FOV. In this paper, we describe a system we have developed that allows both cameras to move and extract the 3D coordinates of the target. This is done with only a single initial calibration between pairs of cameras and high-resolution pan-tilt-zoom (PTZ) platforms. Using the information from the PT settings of the PTZ platform as well as the precalibrated settings from a preset zoom lens, the 3D coordinates of the target are extracted and compared to those of a laser range finder and static-dynamic camera pair accuracies.展开更多
基金the Social Development Project of Jiangsu Key R&D Program(BE2022680)the National Natural Science Foundation of China(Nos.62371253,52278119).
文摘This paper introduces an intelligent computational approach for extracting salient objects fromimages and estimatingtheir distance information with PTZ (Pan-Tilt-Zoom) cameras. PTZ cameras have found wide applications innumerous public places, serving various purposes such as public securitymanagement, natural disastermonitoring,and crisis alarms, particularly with the rapid development of Artificial Intelligence and global infrastructuralprojects. In this paper, we combine Gauss optical principles with the PTZ camera’s capabilities of horizontal andpitch rotation, as well as optical zoom, to estimate the distance of the object.We present a novel monocular objectdistance estimation model based on the Focal Length-Target Pixel Size (FLTPS) relationship, achieving an accuracyrate of over 95% for objects within a 5 km range. The salient object extraction is achieved through a simplifiedconvolution kernel and the utilization of the object’s RGB features, which offer significantly faster computingspeeds compared to Convolutional Neural Networks (CNNs). Additionally, we introduce the dark channel beforethe fog removal algorithm, resulting in a 20 dB increase in image definition, which significantly benefits distanceestimation. Our system offers the advantages of stability and low device load, making it an asset for public securityaffairs and providing a reference point for future developments in surveillance hardware.
文摘This paper presents a real-time, dynamic system that uses high resolution gimbals and motorized lenses with position encoders on their zoom and focus elements to “recalibrate” the system as needed to track a target. Systems that initially calibrate for a mapping between pixels of a wide field of view (FOV) master camera and the pan-tilt (PT) settings of a steerable narrow FOV slave camera assume that the target is travelling on a plane. As the target travels through the FOV of the master camera, the slave cameras PT settings are then adjusted to keep the target centered within its FOV. In this paper, we describe a system we have developed that allows both cameras to move and extract the 3D coordinates of the target. This is done with only a single initial calibration between pairs of cameras and high-resolution pan-tilt-zoom (PTZ) platforms. Using the information from the PT settings of the PTZ platform as well as the precalibrated settings from a preset zoom lens, the 3D coordinates of the target are extracted and compared to those of a laser range finder and static-dynamic camera pair accuracies.
