Control of a PTZ camera in a hybrid vision system

In this paper, we propose a new approach to steer a PTZ camera in the direction of a detected object visible from another fixed camera equipped with a fisheye lens. This heterogeneous association of two cameras having different characteristics is called a hybrid stereo-vision system. The presented method employs epipolar geometry in a smart way in order to reduce the range of search of the desired region of interest. Furthermore, we proposed a target recognition method designed to cope with the illumination problems, the distortion of the omnidirectional image and the inherent dissimilarity of resolution and color responses between both cameras. Experimental results with synthetic and real images show the robustness of the proposed method

Real-Time, Multiple Pan/Tilt/Zoom Computer Vision Tracking and 3D Positioning System for Unmanned Aerial System Metrology

The study of structural characteristics of Unmanned Aerial Systems (UASs) continues to be an important field of research for developing state of the art nano/micro systems. Development of a metrology system using computer vision (CV) tracking and 3D point extraction would provide an avenue for making these theoretical developments. This work provides a portable, scalable system capable of real-time tracking, zooming, and 3D position estimation of a UAS using multiple cameras. Current state-of-the-art photogrammetry systems use retro-reflective markers or single point lasers to obtain object poses and/or positions over time. Using a CV pan/tilt/zoom (PTZ) system has the potential to circumvent their limitations. The system developed in this paper exploits parallel-processing and the GPU for CV-tracking, using optical flow and known camera motion, in order to capture a moving object using two PTU cameras. The parallel-processing technique developed in this work is versatile, allowing the ability to test other CV methods with a PTZ system using known camera motion. Utilizing known camera poses, the object\u27s 3D position is estimated and focal lengths are estimated for filling the image to a desired amount. This system is tested against truth data obtained using an industrial system

Automatic semantic parsing of the ground-plane in scenarios recorded with multiple moving cameras

Nowadays, video surveillance scenarios usually rely on manually annotated focus areas to constrain automatic video analysis tasks. Whereas manual annotation simplifies several stages of the analysis, its use hinders the scalability of the developed solutions and might induce operational problems in scenarios recorded with Multiple and Moving Cameras (MMC). To tackle these problems, an automatic method for the cooperative extraction of Areas of Interest (AoIs) is proposed. Each captured frame is segmented into regions with semantic roles using a stateof- the-art method. Semantic evidences from different junctures, cameras and points-of-view are then spatio-temporally aligned on a common ground plane. Experimental results on widely-used datasets recorded with multiple but static cameras suggest that this process provides broader and more accurate AoIs than those manually defined in the datasets. Moreover, the proposed method naturally determines the projection of obstacles and functional objects in the scene, paving the road towards systems focused on the automatic analysis of human behaviour. To our knowledge, this is the first study dealing with this problematic, as evidenced by the lack of publicly available MMC benchmarks. To also cope with this issue, we provide a new MMC dataset with associated semantic scene annotationsThis study has been partially supported by the Spanish Government through its TEC2014-53176-R HAVideo projec