One of the effective ways to improve object tracking performance is a fusion of base tracking algorithms to their advantages and eliminate disadvantages. This fusion requires the estimation of the performances of the base object tracking algorithms. So the real-time estimation of the performance of each base tracking algorithm is required for the algorithm result to be used for the fusion. In this paper we propose an algorithm for performance estimation for the object tracking algorithm based on the pyramidal implementation of Lukas-Kanade feature tracker.
The performance estimation is based on the analysis of the variations of the intermediate algorithm parameters calculated during object tracking, such as total and mean feature lifetime, eigenvalues, inter-frame mean square coordinate difference, etc. Different combinations of these parameters were tested to obtain the best evaluation quality. The statistic measures were calculated for the image sequence, one or two hundred frames long. These statistic measures are highly correlated with the algorithm performance measures, based on the ground truth data: tracking precision and the ratio of the false detected features. The experimental research was performed using synthetic and real-world image sequences. We investigated performance estimation effectiveness in different observation conditions and during image degradations caused by noise, blur and low contrast.
The experimental results show good performance estimation quality. This allows Lukas-Kanade feature tracker to be fused with another tracking algorithms (correlation-based, segmentation, change detection) to obtain reliable tracking. Since the approach is based on the intermediate Lukas-Kanade algorithm parameters, then it does not bring valuable computational complexity to the tracking process. So real-time performance estimation can be implemented.
Lines are one of the most informative structure elements in any images. For this reason, objects detection and recognition problem are often reduced to edge detection task. Radon transform and Hough transform are widely used in straight-line detection. However, these methods allow estimating only the straight line parameters (but not line segment). It is proposed to split the image into square fragments (blocks) in which straight-line segments are detected to solve this problem. A multi-agent system is used to combine segments into curves and drop false detections. The use of artificial neural networks (NN) for programming a part of agent behavior in the multi-agent system is the main theme of this work.
KEYWORDS: Image processing, Object recognition, Detection and tracking algorithms, Systems modeling, 3D modeling, Field programmable gate arrays, Databases, Unmanned aerial vehicles, Neural networks, Computing systems
This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Lines are one of the most informative structure elements on any images. For this reason, objects detection and recognition problems are often reduced to edge detection task. One of the most popular approaches to detect lines is based on the Hough transform or Radon transform.
However, using both of transforms allows estimating the infinite lines parameters only. It is necessary to use additional approaches to estimate the ends of the detected lines. Moreover, Radon transform does not allow detecting non-straight curve shapes at all. This work is oriented to solve line detection problem using Radon transform and multi-agent approach. The results of the experimental researches that confirm the effectiveness of the proposed approach are given. The real full HD image sequences are used. The direction of further improvements is proposed.
KEYWORDS: 3D modeling, Image processing, 3D image processing, Field programmable gate arrays, Optical spheres, Binary data, Digital signal processing, Cameras, Detection and tracking algorithms, Error analysis
This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.
Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.
This paper describes the implementation of the multiple targets tracking algorithm in FPGA-based vision system. The described algorithm was designed to process such situations as the object trajectories crossing and the temporary object screening by other objects. The source data for this algorithm is a list of the parameters of the previously extracted binary regions from each frame of the sequence. The main idea of this algorithm is to represent the source data as a bipartite graph and split it into insolated elementary graphs corresponding to five situations: object is moving or staying still, a new object detected, object is missed, the pair of the objects is merged into one and the region is divided. These graphs are used to form a new object list. The goal of this work was to implement the described algorithm in small-sized onboard vision system based on the single Xilinx FPGA using MicroBlaze soft processor block. In the proposed implementation of this algorithm recursive procedures were replaced with table-based procedures. The experimental research of the algorithm shows the increasing tracking performance 5 – 9 times on previously described hardware.
In the practice of line detection applications based on Radon transform (RT), noise and clutter decrease the sharpness of the RT local peaks, which correspond to linear edges in the original image. We suggest a new approach to line detection on gray-scale images using the so-called weighted Radon transform (WRT). The proposed WRT-based approach exploits gradient direction information in such a way that only the gradient component perpendicular to the line direction is integrated to form a local peak corresponding to the line. Theoretical and experimental studies have shown the effectiveness of the suggested WRT-based line detection method.
One of the most popular approaches to detect lines is based on the Radon transform (RT). But in real-world applications
RT-based approach suffers from the noise and clutter, because they decrease the sharpness of the local maximums.
In this paper we suggest a new approach to computational effective line detection using the Weighted Radon Transform
(WRT). The suggested WRT-based approach uses gradient direction information, so only the differences that are
perpendicular to the line direction are integrated to make a local maximum corresponding to the line.
The theoretical and experimental studies show the effectiveness of the WRT-based line detection. The suggested WRTbased
algorithm can be effectively implemented in real-time systems using parallelization and FFT-based techniques.
In this paper we propose an object tracking approach that includes the selection of the appropriate coordinate estimation
method from the set of base methods: cross-correlation, statistical segmentation, methods based on spatial and spatiotemporal filtering. The tracking performance of the base methods is estimated by means of the several parameters
(performance features) that describe the reliability of tracking. The features are invariant to the changes of the mean
brightness, contrast, scale and rotation. The comparison of the performance of the methods is based on the binary
performance characteristic that describes the tracking in the terms of work/not work. The proposed object tracking algorithm is implemented in five steps which are carried out on each frame: the calculation of the performance feature for each base coordinate estimation method, the calculation of the binary performance characteristic for each method, the selection of the appropriate method, the estimation of the object coordinates using the selected method, the correction of the tracking process performed by other methods. The algorithm shows a good tracking performance during the variations of the observation conditions. In most cases it comprises the main advantages of each of the source methods.
In this work an algorithm of the tracking of the set of moving objects is described. The important features of the task are
crossings of the object trajectories and temporary screening of the objects by other objects. The source data for the
proposed algorithm is a list of the parameters of the binary regions extracted from each image of the sequence. The main
idea of the considered algorithm is to build a bipartite graph. The recoursive procedure is used to partition the graph into
connected graphs corresponding to five situations: detection of a new object, missing object, merging of the objects into
one region, division of the region and "simple" object tracking. These graphs are used to form a new list of the objects.
The experimental research of the algorithm shows a good tracking performance in both ground and aerial environments.
The work is addressed to the problem of an object extraction in the images during sensor motion. In the case the object
extraction requires the registration of the observed image and the reference background image. The error of the
registration causes false alarms in the extraction result. In this paper the problem of object extraction during sensor
motion is solved by taking into the consideration the statistical properties of this error. The solution of this problem has
been obtained using the method based on Johnson distribution parameters estimation. This method has very high
computational complexity; therefore, the simplified algorithm for an object extraction was developed. The experimental
research results are also presented.
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