Camera calibration in sport event scenarios

The main goal of this paper is the design of a novel and robust methodology for calibrating cameras from a single image in sport scenarios, such as a soccer field, or a basketball or tennis court. In these sport scenarios, the only references we use to calibrate the camera are the lines and circles delimiting the different regions. The first problem we address is the extraction of image primitives, including the challenging problems of shaded regions and lens distortion. From these primitives, we automatically recognise the location of the sport court in the scene by estimating the homography which matches the actual court with its projection onto the image. This is achieved even when only a few primitives are available. Finally, from this homography, we recover the camera calibration parameters. In particular, we estimate the focal length as well as the position and orientation in the 3D space. We present some experiments on models and real courts which illustrate the accuracy of the proposed methodology

Adaptive-Rate Compressive Sensing Using Side Information

We provide two novel adaptive-rate compressive sensing (CS) strategies for sparse, time-varying signals using side information. Our first method utilizes extra cross-validation measurements, and the second one exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients that comprise the images in the video sequence. Instead, we use the side information to predict the number of significant coefficients in the signal at the next time instant. For each image in the video sequence, our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences

Acquiring multi-scale images by pan-tilt-zoom control and automatic multi-camera calibration

This paper describes a system for automatically acquiring high-resolution images by steering a pan-tilt-zoom camera at targets detected in a fixed camera view. The system uses a novel method to automatically calibrate between multiple cameras, estimating the homography between the cameras in a home position, together with the effects of pan and tilt controls and the expected height of a person in the image. These calibrations are chained together to steer a slave camera. In addition we describe a simple manual calibration scheme. 1

Non-myopic information theoretic sensor management of a single pan\u2013tilt\u2013zoom camera for multiple object detection and tracking

Detailed derivation of an information theoretic framework for real PTZ management.Introduction and implementation of a non-myopic strategy.Large experimental validation, with synthetic and realistic datasets.Working demonstration of myopic strategy on an off-the-shelf PTZ camera. Automatic multiple object tracking with a single pan-tilt-zoom (PTZ) cameras is a hard task, with few approaches in the literature, most of them proposing simplistic scenarios. In this paper, we present a novel PTZ camera management framework in which at each time step, the next camera pose (pan, tilt, focal length) is chosen to support multiple object tracking. The policy can be myopic or non-myopic, where the former analyzes exclusively the current frame for deciding the next camera pose, while the latter takes into account plausible future target displacements and camera poses, through a multiple look-ahead optimization. In both cases, occlusions, a variable number of subjects and genuine pedestrian detectors are taken into account, for the first time in the literature. Convincing comparative results on synthetic data, realistic simulations and real trials validate our proposal, showing that non-myopic strategies are particularly suited for a PTZ camera management

Adaptive Sensing and Processing for Some Computer Vision Problems

This dissertation is concerned with adaptive sensing and processing in computer vision, specifically through the application of computer vision techniques to non-standard sensors. In the first part, we adapt techniques designed to solve the classical computer vision problem of gradient-based surface reconstruction to the problem of phase unwrapping that presents itself in applications such as interferometric synthetic aperture radar. Specifically, we propose a new formulation of and solution to the classical two-dimensional phase unwrapping problem. As is usually done, we use the wrapped principal phase gradient field as a measurement of the absolute phase gradient field. Since this model rarely holds in practice, we explicitly enforce integrability of the gradient measurements through a sparse error-correction model. Using a novel energy-minimization functional, we formulate the phase unwrapping task as a generalized lasso problem. We then jointly estimate the absolute phase and the sparse measurement errors using the alternating direction method of multipliers (ADMM) algorithm. Using an interferometric synthetic aperture radar noise model, we evaluate our technique for several synthetic surfaces and compare the results to recently-proposed phase unwrapping techniques. Our method applies new ideas from convex optimization and sparse regularization to this well-studied problem. In the second part, we consider the problem of controlling and processing measurements from a non-traditional, compressive sensing (CS) camera in real time. We focus on how to control the number of measurements it acquires such that this number remains proportional to the amount of foreground information currently present in the scene under observations. To this end, we provide two novel adaptive-rate CS strategies for sparse, time-varying signals using side information. The first method utilizes extra cross-validation measurements, and the second exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients pertaining to the images that comprise the video sequence. Instead, we use the side information to predict this quantity for each upcoming image. Our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and they adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences. Finally, we consider a problem motivated by the application of active pan-tilt-zoom (PTZ) camera control in response to visual saliency. We extend the classical notion of this concept to multi-image data collected using a stationary PTZ camera by requiring consistency: the property that each saliency map in the set of those that are generated should assign the same saliency value to distinct regions of the environment that appear in more than one image. We show that processing each image independently will often fail to provide a consistent measure of saliency, and that using an image mosaic to quantify saliency suffers from several drawbacks. We then propose ray saliency: a mosaic-free method for calculating a consistent measure of bottom-up saliency. Experimental results demonstrating the effectiveness of the proposed approach are presented

Visual attention models for far-field scene analysis

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 141-146).The amount of information available to an intelligent monitoring system is simply too vast to process in its entirety. One way to address this issue is by developing attentive mechanisms that recognize parts of the input as more interesting than others. We apply this concept to the domain of far-field activity analysis by addressing the problem of determining where to look in a scene in order to capture interesting activity in progress. We pose the problem of attention as an unsupervised learning problem, in which the task is to learn from long-term observation a model of the usual pattern of activity. Such a statistical scene model then makes it possible to detect and attend to examples of unusual activity. We present two data-driven scene modeling approaches. In the first, we model the pattern of individual observations (instances) of moving objects at each scene location as a mixture of Gaussians. In the second approach, we model the pattern of sequences of observations -- tracks -- by grouping them into clusters.We employ a similarity measure that combines comparisons of multiple attributes -- such as size, position, and velocity -- in a principled manner so that only tracks that are spatially similar and have similar attributes at spatially corresponding points are grouped together. We group the tracks using spectral clustering and represent the scene model as a mixture of Gaussians in the spectral embedding space. New examples of activity can be efficiently classified by projection into the embedding space. We demonstrate clustering and unusual activity detection results on a week of activity in the scene (about 40,000 moving object tracks) and show that human perceptual judgments of unusual activity are well-correlated with the statistical model. The human validation suggests that the track-based anomaly detection framework would perform well as a classifier for unusual events. To our knowledge, our work is the first to evaluate a statistical scene modeling and anomaly detection framework against human judgments.by Tomáš Ižo.Ph.D

Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG

Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost$100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed

Design of a Multi-biometric Platform, based on physical traits and physiological measures: Face, Iris, Ear, ECG and EEG

Security and safety is one the main concerns both for governments and for private companies in the last years so raising growing interests and investments in the area of biometric recognition and video surveillance, especially after the sad happenings of September 2001. Outlays assessments of the U.S. government for the years 2001-2005 estimate that the homeland security spending climbed from $56.0 billions of dollars in 2001 to almost$100 billion of 2005. In this lapse of time, new pattern recognition techniques have been developed and, even more important, new biometric traits have been investigated and refined; besides the well-known physical and behavioral characteristics, also physiological measures have been studied, so providing more features to enhance discrimination capabilities of individuals. This dissertation proposes the design of a multimodal biometric platform, FAIRY, based on the following biometric traits: ear, face, iris EEG and ECG signals. In the thesis the modular architecture of the platform has been presented, together with the results obtained for the solution to the recognition problems related to the different biometrics and their possible fusion. Finally, an analysis of the pattern recognition issues concerning the area of videosurveillance has been discussed

Systems and Algorithms for Automated Collaborative Observation using Networked Robotic Cameras

The development of telerobotic systems has evolved from Single Operator Single Robot (SOSR) systems to Multiple Operator Multiple Robot (MOMR) systems. The relationship between human operators and robots follows the master-slave control architecture and the requests for controlling robot actuation are completely generated by human operators. Recently, the fast evolving advances in network and computer technologies and decreasing size and cost of sensors and robots enable us to further extend the MOMR system architecture to incorporate heterogeneous components such as humans, robots, sensors, and automated agents. The requests for controlling robot actuation are generated by all the participants. We term it as the MOMR++ system. However, to reach the best potential and performance of the system, there are many technical challenges needing to be addressed. In this dissertation, we address two major challenges in the MOMR++ system development. We first address the robot coordination and planning issue in the application of an autonomous crowd surveillance system. The system consists of multiple robotic pan-tilt-zoom (PTZ) cameras assisted with a fixed wide-angle camera. The wide-angle camera provides an overview of the scene and detects moving objects, which are required for close-up views using the PTZ cameras. When applied to the pedestrian surveillance application and compared to a previous work, the system achieves increasing number of observed objects by over 210% in heavy traffic scenarios. The key issue here is given the limited number (e.g., p (p > 0)) of PTZ cameras and many more (e.g., n (n >> p)) observation requests, how to coordinate the cameras to best satisfy all the requests. We formulate this problem as a new camera resource allocation problem. Given p cameras, n observation requests, and [epsilon] being approximation bound, we develop an approximation algorithm running in O(n/[epsilon]³ + p²/[epsilon]⁶) time, and an exact algorithm, when p = 2, running in O(n³) time. We then address the automatic object content analysis and recognition issue in the application of an autonomous rare bird species detection system. We set up the system in the forest near Brinkley, Arkansas. The camera monitors the sky, detects motions, and preserves video data for only those targeted bird species. During the one-year search, the system reduces the raw video data of 29.41TB to only 146.7MB (reduction rate 99.9995%). The key issue here is to automatically recognize the flying bird species. We verify the bird body axis dynamic information by an extended Kalman filter (EKF) and compare the bird dynamic state with the prior knowledge of the targeted bird species. We quantify the uncertainty in recognition due to the measurement uncertainty and develop a novel Probable Observation Data Set (PODS)-based EKF method. In experiments with real video data, the algorithm achieves 95% area under the receiver operating characteristic (ROC) curve. Through the exploration of the two MOMR++ systems, we conclude that the new MOMR++ system architecture enables much wider range of participants, enhances the collaboration and interaction between participants so that information can be exchanged in between, suppresses the chance of any individual bias or mistakes in the observation process, and further frees humans from the control/observation process by providing automatic control/observation. The new MOMR++ system architecture is a promising direction for future telerobtics advances