| 1. |
- Barceló, Guillem Casas, 1984-, et al.
(författare)
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Image-Based Floor Segmentation in Visual Inertial Navigation
- 2013
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Ingår i: 2013 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - New York : IEEE. - 9781467346214 ; , s. 1402-1407
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Konferensbidrag (refereegranskat)abstract
- This paper presents a floor segmentation algorithmfor indoor sequences that works with single grey-scale images.The portion of the floor closest to the camera is segmentedby judiciously joining a set of horizontal and vertical lines,previously detected. Since the proposed method is not based oncomputing the vanishing point, the system can deal with anykind of indoor scenes and adapts quickly to camera movements.A second contribution is the detection of moving features forpoints within the segmented floor area. Based on the estimatedcamera ego-motion, the ground plane homography is derived.Then, the expected optical flow for the ground points is calculatedand used for rejecting features that belong to moving obstacles.A key point of the designed method is that no restrictions on thecamera motion are imposed for the homography derivation.
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| 2. |
- Dani, Ashwin, et al.
(författare)
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Image Moments for Higher-Level Feature Based Navigation
- 2013
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Ingår i: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). - 9781467363587 ; , s. 602-609
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel vision-based localization and mapping algorithm using image moments of region features. The environment is represented using regions, such as planes and/or 3D objects instead of only a dense set of feature points. The regions can be uniquely defined using a small number of parameters; e.g., a plane can be completely characterized by normal vector and distance to a local coordinate frame attached to the plane. The variation of image moments of the regions in successive images can be related to the parameters of the regions. Instead of tracking a large number of feature points, variations of image moments of regions can be computed by tracking the segmented regions or a few feature points on the objects in successive images. A map represented by regions can be characterized using a minimal set of parameters. The problem is formulated as a nonlinear filtering problem. A new discrete-time nonlinear filter based on the state-dependent coefficient (SDC) form of nonlinear functions is presented. It is shown via Monte-Carlo simulations that the new nonlinear filter is more accurate and consistent than EKF by evaluating the root-mean squared error (RMSE) and normalized estimation error squared (NEES).
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| 3. |
- Innocenti, Christopher, et al.
(författare)
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Imitation Learning for Vision-based Lane Keeping Assistance
- 2017
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Ingår i: IEEE International Conference on Intelligent Transportation Systems-ITSC. - 2153-0009. - 9781538615263 ; 2018-March
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Konferensbidrag (refereegranskat)abstract
- This paper aims to investigate direct imitation learning from human drivers for the task of lane keeping assistance in highway and country roads using grayscale images from a single front view camera. The employed method utilizes convolutional neural networks (CNN) to act as a policy that is driving a vehicle. The policy is successfully learned via imitation learning using real-world data collected from human drivers and is evaluated in closed-loop simulated environments, demonstrating good driving behaviour and a robustness for domain changes. Evaluation is based on two proposed performance metrics measuring how well the vehicle is positioned in a lane and the smoothness of the driven trajectory.
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| 4. |
- Mohammadiha, Nasser, et al.
(författare)
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A state-space approach to dynamic nonnegative matrix factorization
- 2015
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Ingår i: IEEE Transactions on Signal Processing. - : IEEE Signal Processing Society. - 1053-587X .- 1941-0476. ; 63:4, s. 949-959
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Tidskriftsartikel (refereegranskat)abstract
- Nonnegative matrix factorization (NMF) has been actively investigated and used in a wide range of problems in the past decade. A significant amount of attention has been given to develop NMF algorithms that are suitable to model time series with strong temporal dependencies. In this paper, we propose a novel state-space approach to perform dynamic NMF (D-NMF). In the proposed probabilistic framework, the NMF coefficients act as the state variables and their dynamics are modeled using a multi-lag nonnegative vector autoregressive (N-VAR) model within the process equation. We use expectation maximization and propose a maximum-likelihood estimation framework to estimate the basis matrix and the N-VAR model parameters. Interestingly, the N-VAR model parameters are obtained by simply applying NMF. Moreover, we derive a maximum a posteriori estimate of the state variables (i.e., the NMF coefficients) that is based on a prediction step and an update step, similarly to the Kalman filter. We illustrate the benefits of the proposed approach using different numerical simulations where D-NMF significantly outperforms its static counterpart. Experimental results for three different applications show that the proposed approach outperforms two state-of-the-art NMF approaches that exploit temporal dependencies, namely a nonnegative hidden Markov model and a frame stacking approach, while it requires less memory and computational power.
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| 5. |
- Panahandeh, Ghazaleh, et al.
(författare)
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A Fast and Adaptive Boundary Matching Algorithm for Video Error Concealment
- 2010
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Ingår i: 4th International Conference on Signal Processing and Communication Systems, ICSPCS'2010. - : IEEE.
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Konferensbidrag (refereegranskat)abstract
- Low-complexity error concealment techniques for missing macroblock (MB) recovery based on the boundary matching principle are extensively studied and evaluated. In this paper, an improved boundary matching algorithm (BMA) using adaptive search is presented to conceal channel errors in inter-frames of video images. The proposed scheme adaptively selects proper candidate regions to conceal the artifact of a lost block. The candidate regions are examined based on analyzing motion activity of the neighboring MBs. Simulations show that the proposed scheme outperforms both on PSNR and visual quality obviously of about 1?4dB compared to existing methods.
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| 6. |
- Panahandeh, Ghazaleh, et al.
(författare)
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Calibration of an IMU-Camera Cluster Using Planar Mirror Reflection and Its Observability Analysis
- 2015
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE Press. - 0018-9456 .- 1557-9662. ; 64:1, s. 75-88
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a novel and a low-cost calibration approach to estimate the relative transformation between an inertial measurement unit (IMU) and a camera, which are rigidly mounted together. The calibration is performed by fusing the measurements from the IMU-camera rig moving in front of a planar mirror. To construct the visual observations, we select a set of key features (KFs) attached to the visual inertial rig where the 3-D positions of the KFs are unknown. During calibration, the system is navigating in front of the planar mirror, while the vision sensor observes the reflections of the KFs in the mirror, and the inertial sensor measures the system's linear accelerations and rotational velocities over time. Our first contribution in this paper is studying the observability properties of IMU-camera calibration parameters. For this visual inertial calibration problem, we derive its time-varying nonlinear state-space model and study its observability properties using the Lie derivative rank condition test. We show that the calibration parameters and the 3-D position of the KFs are observable. As our second contribution, we propose an approach for estimating the calibration parameters along with the 3-D position of the KFs and the dynamics of the analyzed system. The estimation problem is then solved in the unscented Kalman filter framework. We illustrate the findings of our theoretical analysis using both simulations and experiments. The achieved performance indicates that our proposed method can conveniently be used in consumer products like visual inertial-based applications in smartphones for localization, 3-D reconstruction, and surveillance applications.
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| 7. |
- Panahandeh, Ghazaleh, et al.
(författare)
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Calibration of the Accelerometer Triad of an Inertial Measurement Unit, Maximum Likelihood Estimation and Cramer-Rao Bound
- 2010
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Ingår i: International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zurich, September 15-17, 2010. - : IEEE. - 9781424458646 ; , s. 1-6
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Konferensbidrag (refereegranskat)abstract
- In this paper, a simple method to calibrate the accelerometer cluster of an inertial measurement unit (IMU) is proposed. The method does not rely on using a mechanical calibration platform that rotates the IMU into different precisely controlled orientations. Although the IMU is rotated into different orientations, these orientations do not need to be known. Assuming that the IMU is stationary at each orientation, the norm of the input is considered equal to the gravity acceleration. As the orientations of the IMU are unknown, the calibration of the accelerometer cluster is stated as a blind system identification problem where only the norm of the input to the system is known. Under the assumption that the sensor noises have a white Gaussian distribution the system identification problem is solved using the maximum likelihood estimation method. The accuracy of the proposed calibration method is compared with the Cram´er- Rao bound for the considered calibration problem.
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| 8. |
- Panahandeh, Ghazaleh, et al.
(författare)
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Chest-Mounted Inertial Measurement Unit for Pedestrian Motion Classification Using Continuous Hidden Markov Model
- 2012
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Ingår i: 2012 IEEE International Instrumentation and Measurement Technology Conference (I2MTC). - : IEEE. ; , s. 991-995
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Konferensbidrag (refereegranskat)abstract
- This paper presents a method for pedestrian motionclassification based on MEMS inertial measurement unit (IMU)mounted on the chest. The choice of mounting the IMU on thechest provides the potential application of the current study incamera-aided inertial navigation for positioning and personalassistance. In the present work, five categories of the pedestrianmotion including standing, walking, running, going upstairs,and going down the stairs are considered in the classificationprocedure. As the classification method, the continuous hiddenMarkov model (HMM) is used in which the output densityfunctions are assumed to be Gaussian mixture models (GMMs).The correct recognition rates based on the experimental resultsare about 95%.
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| 9. |
- Panahandeh, Ghazaleh, et al.
(författare)
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Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis
- 2013
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Ingår i: IEEE Transactions on Instrumentation and Measurement. - : IEEE Press. - 0018-9456 .- 1557-9662. ; 62:5, s. 1073-1083
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a "circular HMM." For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications.
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| 10. |
- Panahandeh, Ghazaleh, 1984-, et al.
(författare)
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Exploiting Ground Plane Constraints for Visual-Inertial Navigation
- 2012
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Ingår i: 2012 IEEE/ION Position Location and Navigation Symposium (PLANS). - : IEEE. - 9781467303866 ; , s. 527-534
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Konferensbidrag (refereegranskat)abstract
- In this paper, an ego-motion estimation approach is introduced that fuses visual and inertial information, using a monocular camera and an inertial measurement unit. The system maintains a set of feature points that are observed on the ground plane. Based on matched feature points between the current and previous images, a novel measurement model is introduced that imposes visual constraints on the inertial navigation system to perform 6 DoF motion estimation. Furthermore, feature points are used to impose epipolar constraints on the estimated motion between current and past images. Pose estimation is formulated implicitly in a state-space framework and is performed by a Sigma-Point Kalman filter. The presented experiments, conducted in an indoor scenario with real data, indicate the ability of the proposed method to perform accurate 6 DoF pose estimation.
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