Vehicle detection and tracking using homography-based plane rectification and particle filtering

This paper presents a full system for vehicle detection and tracking in non-stationary settings based on computer vision. The method proposed for vehicle detection exploits the geometrical relations between the elements in the scene so that moving objects (i.e., vehicles) can be detected by analyzing motion parallax. Namely, the homography of the road plane between successive images is computed. Most remarkably, a novel probabilistic framework based on Kalman filtering is presented for reliable and accurate homography estimation. The estimated homography is used for image alignment, which in turn allows to detect the moving vehicles in the image. Tracking of vehicles is performed on the basis of a multidimensional particle filter, which also manages the exit and entries of objects. The filter involves a mixture likelihood model that allows a better adaptation of the particles to the observed measurements. The system is specially designed for highway environments, where it has been proven to yield excellent results

Automatic aerial target detection and tracking system in airborne FLIR images based on efficient target trajectory filtering

Common strategies for detection and tracking of aerial moving targets in airborne Forward-Looking Infrared (FLIR) images offer accurate results in images composed by a non-textured sky. However, when cloud and earth regions appear in the image sequence, those strategies result in an over-detection that increases very significantly the false alarm rate. Besides, the airborne camera induces a global motion in the image sequence that complicates even more detection and tracking tasks. In this work, an automatic detection and tracking system with an innovative and efficient target trajectory filtering is presented. It robustly compensates the global motion to accurately detect and track potential aerial targets. Their trajectories are analyzed by a curve fitting technique to reliably validate real targets. This strategy allows to filter false targets with stationary or erratic trajectories. The proposed system makes special emphasis in the use of low complexity video analysis techniques to achieve real-time operation. Experimental results using real FLIR sequences show a dramatic reduction of the false alarm rate, while maintaining the detection rate

Dynamic Denoising of Tracking Sequences

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.920795In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences.Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other

The use of the Kalman filter in the automated segmentation of EIT lung images

In this paper, we present a new pipeline for the fast and accurate segmentation of impedance images of the lungs using electrical impedance tomography (EIT). EIT is an emerging, promising, non-invasive imaging modality that produces real-time, low spatial but high temporal resolution images of impedance inside a body. Recovering impedance itself constitutes a nonlinear ill-posed inverse problem, therefore the problem is usually linearized, which produces impedance-change images, rather than static impedance ones. Such images are highly blurry and fuzzy along object boundaries. We provide a mathematical reasoning behind the high suitability of the Kalman filter when it comes to segmenting and tracking conductivity changes in EIT lung images. Next, we use a two-fold approach to tackle the segmentation problem. First, we construct a global lung shape to restrict the search region of the Kalman filter. Next, we proceed with augmenting the Kalman filter by incorporating an adaptive foreground detection system to provide the boundary contours for the Kalman filter to carry out the tracking of the conductivity changes as the lungs undergo deformation in a respiratory cycle. The proposed method has been validated by using performance statistics such as misclassified area, and false positive rate, and compared to previous approaches. The results show that the proposed automated method can be a fast and reliable segmentation tool for EIT imaging

Robust automatic target tracking based on a Bayesian ego-motion compensation framework for airborne FLIR imagery

Automatic target tracking in airborne FLIR imagery is currently a challenge due to the camera ego-motion. This phenomenon distorts the spatio-temporal correlation of the video sequence, which dramatically reduces the tracking performance. Several works address this problem using ego-motion compensation strategies. They use a deterministic approach to compensate the camera motion assuming a specific model of geometric transformation. However, in real sequences a specific geometric transformation can not accurately describe the camera ego-motion for the whole sequence, and as consequence of this, the performance of the tracking stage can significantly decrease, even completely fail. The optimum transformation for each pair of consecutive frames depends on the relative depth of the elements that compose the scene, and their degree of texturization. In this work, a novel Particle Filter framework is proposed to efficiently manage several hypothesis of geometric transformations: Euclidean, affine, and projective. Each type of transformation is used to compute candidate locations of the object in the current frame. Then, each candidate is evaluated by the measurement model of the Particle Filter using the appearance information. This approach is able to adapt to different camera ego-motion conditions, and thus to satisfactorily perform the tracking. The proposed strategy has been tested on the AMCOM FLIR dataset, showing a high efficiency in the tracking of different types of targets in real working conditions

Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion

This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and tracked using the improved Gaussian mixture models (GMM) and Kalman filtering. New fusion rules are introduced which work on the magnitudes and angles of the DT-CWT coefficients independently to achieve a sharp image and to reduce atmospheric distortion, respectively. The subjective results show that the proposed method achieves better video quality than other existing methods with competitive speed.Comment: IEEE International Conference on Image Processing 201

A Fusion Approach for Multi-Frame Optical Flow Estimation

To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account. While elegant and appealing, the idea of using more than two frames has not yet produced state-of-the-art results. We present a simple, yet effective fusion approach for multi-frame optical flow that benefits from longer-term temporal cues. Our method first warps the optical flow from previous frames to the current, thereby yielding multiple plausible estimates. It then fuses the complementary information carried by these estimates into a new optical flow field. At the time of writing, our method ranks first among published results in the MPI Sintel and KITTI 2015 benchmarks. Our models will be available on https://github.com/NVlabs/PWC-Net.Comment: Work accepted at IEEE Winter Conference on Applications of Computer Vision (WACV 2019

Knee Joint Angle Measuring Portable Embedded System based on Inertial Measurement Units for Gait Analysis

Inside clinical research, gait analysis is a fundamental part of the functional evaluation of the human body's movement. Its evaluation has been carried out through different methods and tools, which allow early diagnosis of diseases, and monitoring and assessing the effectiveness of therapeutic plans applied to patients for rehabilitation. The observational method is one of the most used in specialized centers in Colombia; however, to avoid any possible errors associated with the subjectivity observation, technological tools that provide quantitative data can support this method. This paper deals with the methodological process for developing a computational tool and hardware device for the analysis of gait, specifically on articular kinematics of the knee.  This work develops a prototype based on the fusion of inertial measurement units (IMU) data as an alternative for the attenuation of errors associated with each of these technologies. A videogrammetry technique measured the same human gait patterns to validate the proposed system, in terms of accuracy and repeatability of the recorded data. Results showed that the developed prototype successfully captured the knee-joint angles of the flexion-extension motions with high consistency and accuracy in with the measurements obtained from the videogrammetry technique. Statistical analysis (ICC and RMSE) exhibited a high correlation between the two systems for the measures of the joint angles. These results suggest the possibility of using an IMU-based prototype in realistic scenarios for accurately tracking a patient’s knee-joint kinematics during a human gait