Towards A Self-calibrating Video Camera Network For Content Analysis And Forensics

Due to growing security concerns, video surveillance and monitoring has received an immense attention from both federal agencies and private firms. The main concern is that a single camera, even if allowed to rotate or translate, is not sufficient to cover a large area for video surveillance. A more general solution with wide range of applications is to allow the deployed cameras to have a non-overlapping field of view (FoV) and to, if possible, allow these cameras to move freely in 3D space. This thesis addresses the issue of how cameras in such a network can be calibrated and how the network as a whole can be calibrated, such that each camera as a unit in the network is aware of its orientation with respect to all the other cameras in the network. Different types of cameras might be present in a multiple camera network and novel techniques are presented for efficient calibration of these cameras. Specifically: (i) For a stationary camera, we derive new constraints on the Image of the Absolute Conic (IAC). These new constraints are shown to be intrinsic to IAC; (ii) For a scene where object shadows are cast on a ground plane, we track the shadows on the ground plane cast by at least two unknown stationary points, and utilize the tracked shadow positions to compute the horizon line and hence compute the camera intrinsic and extrinsic parameters; (iii) A novel solution to a scenario where a camera is observing pedestrians is presented. The uniqueness of formulation lies in recognizing two harmonic homologies present in the geometry obtained by observing pedestrians; (iv) For a freely moving camera, a novel practical method is proposed for its self-calibration which even allows it to change its internal parameters by zooming; and (v) due to the increased application of the pan-tilt-zoom (PTZ) cameras, a technique is presented that uses only two images to estimate five camera parameters. For an automatically configurable multi-camera network, having non-overlapping field of view and possibly containing moving cameras, a practical framework is proposed that determines the geometry of such a dynamic camera network. It is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the geometry of a dynamic network. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic as well as on real data. Applications to path modeling, GPS coordinate estimation, and configuring mixed-reality environment are explored

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management

Estimating Geo-temporal Location of Stationary Cameras Using Shadow Trajectories

Abstract. Using only shadow trajectories of stationary objects in a scene, we demonstrate that using a set of six or more photographs are sufficient to ac-curately calibrate the camera. Moreover, we present a novel application where, using only three points from the shadow trajectory of the objects, one can ac-curately determine the geo-location of the camera, up to a longitude ambiguity, and also the date of image acquisition without using any GPS or other special instruments. We refer to this as “geo-temporal localization”. We consider possi-ble cases where ambiguities can be removed if additional information is avail-able. Our method does not require any knowledge of the date or the time when the pictures are taken, and geo-temporal information is recovered directly from the images. We demonstrate the accuracy of our technique for both steps of cali-bration and geo-temporal localization using synthetic and real data.

Landslide monitoring by fixed-base terrestrial stereo-photogrammetry

Photogrammetry has been used since long to periodically control the evolution of landslides; however, true monitoring is reserved to robotic total stations and ground based InSAR systems, capable of high frequency, high accurate 24h/day response. This paper presents the first results of a fixed terrestrial stereo photogrammetric system developed to monitor shape changes of the scene. The system is made of two reflex cameras, each contained in a sealed box with a control computer that periodically acquires an image and send it to a host computer; once an image pair is received from the two cameras, the DSM of the scene is generated by image correlation and made available for archiving or analysis. The system has been installed and is being tested on the Mont de la Saxe landslide, where several monitoring system are active. Some instability of the camera attitude has been noticed and is corrected with an automated procedure. First comparisons with InSAR data show a good agreement

Landslide monitoring by fixed-base terrestrial stereo-photogrammetry

Photogrammetry has been used since long to periodically control the evolution of landslides; however, true monitoring is reserved to robotic total stations and ground based InSAR systems, capable of high frequency, high accurate 24h/day response. This paper presents the first results of a fixed terrestrial stereo photogrammetric system developed to monitor shape changes of the scene. The system is made of two reflex cameras, each contained in a sealed box with a control computer that periodically acquires an image and send it to a host computer; once an image pair is received from the two cameras, the DSM of the scene is generated by image correlation and made available for archiving or analysis. The system has been installed and is being tested on the Mont de la Saxe landslide, where several monitoring system are active. Some instability of the camera attitude has been noticed and is corrected with an automated procedure. First comparisons with InSAR data show a good agreement