Robust real-time ground plane motion compensation from a moving vehicle

One method to detect obstacles from a vehicle moving on a planar road surface is the analysis of motion-compensated difference images. In this contribution, a motion compensation algorithm is presented, which computes the required image-warping parameters from an estimate of the relative motion between camera and ground plane. The proposed algorithm estimates the warping parameters from displacements at image corners and image edges. It exploits the estimated confidence of the displacements to cope robustly with outliers. Knowledge about camera calibration, measuremts from odometry, and the previous estimate are used for motion prediction and to stabilize the estimation process when there is not enough information available in the measured image displacements. The motion compensation algorithm has been integrated with modules for obstacle detection and lane tracking. This system has been integrated in experimental vehicles and runs in real time with an overall cycle of 12.5 Hz on low-cost standard hardware. Received: 23 April 1998 / Accepted: 25 August 1999     

Joint ego-motion and road geometry estimation

We provide a sensor fusion framework for solving the problem of joint ego-motion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.     

Optic flow estimation by a Hopfield neural network using geometrical constraints

Sparse optic flow maps are general enough to obtain useful information about camera motion. Usually, correspondences among features over an image sequence are estimated by radiometric similarity. When the camera moves under known conditions, global geometrical constraints can be introduced in order to obtain a more robust estimation of the optic flow. In this paper, a method is proposed for the computation of a robust sparse optic flow (OF) which integrates the geometrical constraints induced by camera motion to verify the correspondences obtained by radiometric-similarity-based techniques. A raw OF map is estimated by matching features by correlation. The verification of the resulting correspondences is formulated as an optimization problem that is implemented on a Hopfield neural network (HNN). Additional constraints imposed in the energy function permit us to achieve a subpixel accuracy in the image locations of matched features. Convergence of the HNN is reached in a small enough number of iterations to make the proposed method suitable for real-time processing. It is shown that the proposed method is also suitable for identifying independently moving objects in front of a moving vehicle. Received: 26 December 1995 / Accepted: 20 February 1997     

Tracking the human arm using constraint fusion and multiple-cue localization

Abstract. The use of hand gestures provides an attractive means of interacting naturally with a computer-generated display. Using one or more video cameras, the hand movements can potentially be interpreted as meaningful gestures. One key problem in building such an interface without a restricted setup is the ability to localize and track the human arm robustly in video sequences. This paper proposes a multiple-cue localization scheme combined with a tracking framework to reliably track the dynamics of the human arm in unconstrained environments. The localization scheme integrates the multiple cues of motion, shape, and color for locating a set of key image features. Using constraint fusion, these features are tracked by a modified extended Kalman filter that exploits the articulated structure of the human arm. Moreover, an interaction scheme between tracking and localization is used for improving the estimation process while reducing the computational requirements. The performance of the localization/tracking framework is validated with the help of extensive experiments and simulations. These experiments include tracking with calibrated stereo camera and uncalibrated broadcast video. Received: 19 January 2001 / Accepted: 27 December 2001 Correspondence to: R. Sharma     

Mobile robot navigation and scene modeling using stereo fish-eye lens system

We present an autonomous mobile robot navigation system using stereo fish-eye lenses for navigation in an indoor structured environment and for generating a model of the imaged scene. The system estimates the three-dimensional (3D) position of significant features in the scene, and by estimating its relative position to the features, navigates through narrow passages and makes turns at corridor ends. Fish-eye lenses are used to provide a large field of view, which images objects close to the robot and helps in making smooth transitions in the direction of motion. Calibration is performed for the lens-camera setup and the distortion is corrected to obtain accurate quantitative measurements. A vision-based algorithm that uses the vanishing points of extracted segments from a scene in a few 3D orientations provides an accurate estimate of the robot orientation. This is used, in addition to 3D recovery via stereo correspondence, to maintain the robot motion in a purely translational path, as well as to remove the effects of any drifts from this path from each acquired image. Horizontal segments are used as a qualitative estimate of change in the motion direction and correspondence of vertical segment provides precise 3D information about objects close to the robot. Assuming detected linear edges in the scene as boundaries of planar surfaces, the 3D model of the scene is generated. The robot system is implemented and tested in a structured environment at our research center. Results from the robot navigation in real environments are presented and discussed. Received: 25 September 1996 / Accepted: 20 October 1996     

Development and Research of the Mathematical Model of Planar Motion of a Vehicle with a Semitrailer

On horizontal nondeformable ground, we consider the movement of a road train consisting of a biaxial tractor car and triaxial semitrailer treated as solid bodies. Based on the Lagrange’s equations of the second kind, we develop a nonlinear mathematical model of its plane motion, using the position of the fifth-wheel coupling and rotating angles of the tractor and semitrailer body as generalized coordinates. We analyze and linearize the constructed system of equations and obtain a linear mathematical model describing the small lateral displacements and rotations of the elements of a road train when it is moving at a high longitudinal speed, small jackknifing angle, and small rotation angle of the steering wheels. Using the equivalent transformations of the obtained system of equations, we construct a state-space linear model of the lateral motion of the road train. A comparative analysis of the use of linear and nonlinear models to describe the road train’s motion, carrying out standard maneuvers, is performed. It is shown that, if the restrictions are satisfied, then the results of nonlinear and linear model usage are quite close to each other and sufficiently well agree with the results of the field tests. The developed model, unlike the already known ones, is fairly simple (linear). Further, it could be used for an analytical synthesis of the control laws for the lateral component of the motion of road trains.     

The perceptive workbench: Computer-vision-based gesture tracking,object tracking,and 3D reconstruction for augmented desks

Abstract. The Perceptive Workbench endeavors to create a spontaneous and unimpeded interface between the physical and virtual worlds. Its vision-based methods for interaction constitute an alternative to wired input devices and tethered tracking. Objects are recognized and tracked when placed on the display surface. By using multiple infrared light sources, the object's 3-D shape can be captured and inserted into the virtual interface. This ability permits spontaneity, since either preloaded objects or those objects selected at run-time by the user can become physical icons. Integrated into the same vision-based interface is the ability to identify 3-D hand position, pointing direction, and sweeping arm gestures. Such gestures can enhance selection, manipulation, and navigation tasks. The Perceptive Workbench has been used for a variety of applications, including augmented reality gaming and terrain navigation. This paper focuses on the techniques used in implementing the Perceptive Workbench and the system's performance.     

Real-time multiple vehicle detection and tracking from a moving vehicle

Abstract. A real-time vision system has been developed that analyzes color videos taken from a forward-looking video camera in a car driving on a highway. The system uses a combination of color, edge, and motion information to recognize and track the road boundaries, lane markings and other vehicles on the road. Cars are recognized by matching templates that are cropped from the input data online and by detecting highway scene features and evaluating how they relate to each other. Cars are also detected by temporal differencing and by tracking motion parameters that are typical for cars. The system recognizes and tracks road boundaries and lane markings using a recursive least-squares filter. Experimental results demonstrate robust, real-time car detection and tracking over thousands of image frames. The data includes video taken under difficult visibility conditions. Received: 1 September 1998 / Accepted: 22 February 2000     

A model-driven approach for real-time road recognition

This article describes a method designed to detect and track road edges starting from images provided by an on-board monocular monochromic camera. Its implementation on specific hardware is also presented in the framework of the VELAC project. The method is based on four modules: (1) detection of the road edges in the image by a model-driven algorithm, which uses a statistical model of the lane sides which manages the occlusions or imperfections of the road marking – this model is initialized by an off-line training step; (2) localization of the vehicle in the lane in which it is travelling; (3) tracking to define a new search space of road edges for the next image; and (4) management of the lane numbers to determine the lane in which the vehicle is travelling. The algorithm is implemented in order to validate the method in a real-time context. Results obtained on marked and unmarked road images show the robustness and precision of the method. Received: 18 November 2000 / Accepted: 7 May 2001     

Motion compensated film restoration

Motion picture films are susceptible to local degradations such as dust spots. Other deteriorations are global such as intensity and spatial jitter. It is obvious that motion needs to be compensated for before the detection/correction of such local and dynamic defects. Therefore, we propose a hierarchical motion estimation method ideally suited for high resolution film sequences. This recursive block-based motion estimator relies on an adaptive search strategy and Radon projections to improve processing speed. The localization of dust particles then becomes straightforward. Thus, it is achieved by simple inter-frame differences between the current image and motion compensated successive and preceding frames. However, the detection of spatial and intensity jitter requires a specific process taking advantage of the high temporal correlation in the image sequence. In this paper, we present our motion compensation-based algorithms for removing dust spots, spatial and intensity jitter in degraded motion pictures. Experimental results are presented showing the usefulness of our motion estimator for film restoration at reasonable computational costs. Received: 9 July 2000 / Accepted: 13 January 2002 Correspondence to:S. Boukir     

New adaptive approaches to real-time estimation of vehicle sideslip angle

This paper presents new approaches to the identification of the vehicle sideslip angle and the road bank angle in real-time. The major challenge is that the vehicle sideslip angle and the road bank angle are coupled together with the system uncertainties, such as variations in the vehicle parameters and the tire cornering stiffness. To resolve this difficulty, the proposed estimation algorithms identify the uncertain vehicle parameters using the sensor measurements such as the steering angle, the lateral acceleration and the yaw rate, and then estimate the vehicle sideslip angle and the road bank angle via a simple algebraic relationship in real time. In particular, the use of the lateral G sensor signal makes it possible to identify the cornering stiffness and vehicle sideslip angle without any a priori knowledge on the road bank angle. The performance of the proposed algorithms is verified through simulation and experimental results.     

Document skew estimation without angle range restriction

The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method. Received October 10, 1998 / Revised version September 9, 1999     

The reconstruction of dynamic 3D structure of biological objects using stereo microscope images

In this paper, we address the analysis of 3D shape and shape change in non-rigid biological objects imaged via a stereo light microscope. We propose an integrated approach for the reconstruction of 3D structure and the motion analysis for images in which only a few informative features are available. The key components of this framework are: 1) image registration using a correlation-based approach, 2) region-of-interest extraction using motion-based segmentation, and 3) stereo and motion analysis using a cooperative spatial and temporal matching process. We describe these three stages of processing and illustrate the efficacy of the proposed approach using real images of a live frog's ventricle. The reconstructed dynamic 3D structure of the ventricle is demonstrated in our experimental results, and it agrees qualitatively with the observed images of the ventricle.     

Automatic video data structuring through shot partitioning and key-frame computing

In video processing, a common first step is to segment the videos into physical units, generally called shots. A shot is a video segment that consists of one continuous action. In general, these physical units need to be clustered to form more semantically significant units, such as scenes, sequences, programs, etc. This is the so-called story-based video structuring. Automatic video structuring is of great importance for video browsing and retrieval. The shots or scenes are usually described by one or several representative frames, called key-frames. Viewed from a higher level, key frames of some shots might be redundant in terms of semantics. In this paper, we propose automatic solutions to the problems of: (i) video partitioning, (ii) key frame computing, (iii) key frame pruning. For the first problem, an algorithm called “net comparison” is devised. It is accurate and fast because it uses both statistical and spatial information in an image and does not have to process the entire image. For the last two problems, we develop an original image similarity criterion, which considers both spatial layout and detail content in an image. For this purpose, coefficients of wavelet decomposition are used to derive parameter vectors accounting for the above two aspects. The parameters exhibit (quasi-) invariant properties, thus making the algorithm robust for many types of object/camera motions and scaling variances. The novel “seek and spread” strategy used in key frame computing allows us to obtain a large representative range for the key frames. Inter-shot redundancy of the key-frames is suppressed using the same image similarity measure. Experimental results demonstrate the effectiveness and efficiency of our techniques.     

A golden-template self-generating method for patterned wafer inspection

This paper presents a novel golden-template self-generating technique for detecting possible defects in periodic two-dimensional wafer images. A golden template of the patterned wafer image under inspection can be obtained from the wafer image itself and no other prior knowledge is needed. It is a bridge between the existing self-reference methods and image-to-image reference methods. Spectral estimation is used in the first step to derive the periods of repeating patterns in both di r ections. Then a building block representing the structure of the patterns is extracted using interpolation to obtain sub-pixel resolution. After that, a new defect-free golden template is built based on the extracted building block. Finally, a pixel-to-pixel comparison is all we need to find possible defects. A comparison between the results of the proposed method and those of the previously published methods is presented. Received: 19 May 1999 / Accepted: 20 May 2000     

Evaluation of statistical and multiple-hypothesis tracking for video traffic surveillance

Abstract. Conventional tracking methods encounter difficulties as the number of objects, clutter, and sensors increase, because of the requirement for data association. Statistical tracking, based on the concept of network tomography, is an alternative that avoids data association. It estimates the number of trips made from one region to another in a scene based on interregion boundary traffic counts accumulated over time. It is not necessary to track an object through a scene to determine when an object crosses a boundary. This paper describes statistical tracing and presents an evaluation based on the estimation of pedestrian and vehicular traffic intensities at an intersection over a period of 1 month. We compare the results with those from a multiple-hypothesis tracker and manually counted ground-truth estimates. Received: 30 August 2001 / Accepted: 28 May 2002 Correspondence to: J.E. Boyd     

Formal verification of conflict detection algorithms

Safety assessment of new air traffic management systems is a main issue for civil aviation authorities. Standard techniques such as testing and simulation have serious limitations in new systems that are significantly more autonomous than the older ones. In this paper, we present an innovative approach for establishing the correctness of conflict detection systems. Fundamental to our approach is the concept of trajectory, and how we represent a continuous physical trajectory by a continuous path in the x-y plane constrained by physical laws and operational requirements. From the model of trajectories, we extract, and formally prove, high-level properties that can serve as a framework to analyze conflict scenarios. We use the AILS (Airborne Information for Lateral Spacing) alerting algorithm as a case study of our approach. Published online: 19 November 2002     

Yoda,an adaptive soft classification model: content-based similarity queries and beyond

Abstract. Providing a customized result set based upon a user preference is the ultimate objective of many content-based image retrieval systems. There are two main challenges in meeting this objective: First, there is a gap between the physical characteristics of digital images and the semantic meaning of the images. Secondly, different people may have different perceptions on the same set of images. To address both these challenges, we propose a model, named Yoda, that conceptualizes content-based querying as the task of soft classifying images into classes. These classes can overlap, and their members are different for different users. The “soft” classification is hence performed for each and every image feature, including both physical and semantic features. Subsequently, each image will be ranked based on the weighted aggregation of its classification memberships. The weights are user-dependent, and hence different users would obtain different result sets for the same query. Yoda employs a fuzzy-logic based aggregation function for ranking images. We show that, in addition to some performance benefits, fuzzy aggregation is less sensitive to noise and can support disjunctive queries as compared to weighted-average aggregation used by other content-based image retrieval systems. Finally, since Yoda heavily relies on user-dependent weights (i.e., user profiles) for the aggregation task, we utilize the users' relevance feedback to improve the profiles using genetic algorithms (GA). Our learning mechanism requires fewer user interactions, and results in a faster convergence to the user's preferences as compared to other learning techniques. Correspondence to: Y.-S. Chen (E-mail: yishinc@usc.edu) This research has been funded in part by NSF grants EEC-9529152 (IMSC ERC) and IIS-0082826, NIH-NLM R01-LM07061, DARPA and USAF under agreement nr. F30602-99-1-0524, and unrestricted cash gifts from NCR, Microsoft, and Okawa Foundation.     

Algorithms for coplanar camera calibration

Abstract. Coplanar camera calibration is the process of determining the extrinsic and intrinsic camera parameters from a given set of image and world points, when the world points lie on a two-dimensional plane. Noncoplanar calibration, on the other hand, involves world points that do not lie on a plane. While optimal solutions for both the camera-calibration procedures can be obtained by solving a set of constrained nonlinear optimization problems, there are significant structural differences between the two formulations. We investigate the computational and algorithmic implications of such underlying differences, and provide a set of efficient algorithms that are specifically tailored for the coplanar case. More specifically, we offer the following: (1) four algorithms for coplanar calibration that use linear or iterative linear methods to solve the underlying nonlinear optimization problem, and produce sub-optimal solutions. These algorithms are motivated by their computational efficiency and are useful for real-time low-cost systems. (2) Two optimal solutions for coplanar calibration, including one novel nonlinear algorithm. A constraint for the optimal estimation of extrinsic parameters is also given. (3) A Lyapunov type convergence analysis for the new nonlinear algorithm. We test the validity and performance of the calibration procedures with both synthetic and real images. The results consistently show significant improvements over less complete camera models. Received: 30 September 1998 / Accepted: 12 January 2000     

MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences

In this paper, we present a method called MODEEP (Motion-based Object DEtection and Estimation of Pose) to detect independently moving objects (IMOs) in forward-looking infrared (FLIR) image sequences taken from an airborne, moving platform. Ego-motion effects are removed through a robust multi-scale affine image registration process. Thereafter, areas with residual motion indicate potential object activity. These areas are detected, refined and selected using a Bayesian classifier. The resulting regions are clustered into pairs such that each pair represents one object's front and rear end. Using motion and scene knowledge, we estimate object pose and establish a region of interest (ROI) for each pair. Edge elements within each ROI are used to segment the convex cover containing the IMO. We show detailed results on real, complex, cluttered and noisy sequences. Moreover, we outline the integration of our fast and robust system into a comprehensive automatic target recognition (ATR) and action classification system.