Target Contour Information Research Articles

Tracking of Multiple Static and Dynamic Targets for 4D Automotive Millimeter-Wave Radar Point Cloud in Urban Environments

This paper presents a target tracking algorithm based on 4D millimeter-wave radar point cloud information for autonomous driving applications, which addresses the limitations of traditional 2 + 1D radar systems by using higher resolution target point cloud information that enables more accurate motion state estimation and target contour information. The proposed algorithm includes several steps, starting with the estimation of the ego vehicle’s velocity information using the radial velocity information of the millimeter-wave radar point cloud. Different clustering suggestions are then obtained using a density-based clustering method, and correlation regions of the targets are obtained based on these clustering suggestions. The binary Bayesian filtering method is then used to determine whether the targets are dynamic or static targets based on their distribution characteristics. For dynamic targets, Kalman filtering is used to estimate and update the state of the target using trajectory and velocity information, while for static targets, the rolling ball method is used to estimate and update the shape contour boundary of the target. Unassociated measurements are estimated for the contour and initialized for the trajectory, and unassociated trajectory targets are selectively retained and deleted. The effectiveness of the proposed method is verified using real data. Overall, the proposed target tracking algorithm based on 4D millimeter-wave radar point cloud information has the potential to improve the accuracy and reliability of target tracking in autonomous driving applications, providing more comprehensive motion state and target contour information for better decision making.

Image Enhancement of Maritime Infrared Targets Based on Scene Discrimination.

Infrared image enhancement technology can effectively improve the image quality and enhance the saliency of the target and is a critical component in the marine target search and tracking system. However, the imaging quality of maritime infrared images is easily affected by weather and sea conditions and has low contrast defects and weak target contour information. At the same time, the target is disturbed by different intensities of sea clutter, so the characteristics of the target are also different, which cannot be processed by a single algorithm. Aiming at these problems, the relationship between the directional texture features of the target and the roughness of the sea surface is deeply analyzed. According to the texture roughness of the waves, the image scene is adaptively divided into calm sea surface and rough sea surface. At the same time, through the Gabor filter at a specific frequency and the gradient-based target feature extraction operator proposed in this paper, the clutter suppression and feature fusion strategies are set, and the target feature image of multi-scale fusion in two types of scenes are obtained, which is used as a guide image for guided filtering. The original image is decomposed into a target and a background layer to extract the target features and avoid image distortion. The blurred background around the target contour is extracted by Gaussian filtering based on the potential target region, and the edge blur caused by the heat conduction of the target is eliminated. Finally, an enhanced image is obtained by fusing the target and background layers with appropriate weights. The experimental results show that, compared with the current image enhancement method, the method proposed in this paper can improve the clarity and contrast of images, enhance the detectability of targets in distress, remove sea surface clutter while retaining the natural environment features in the background, and provide more information for target detection and continuous tracking in maritime search and rescue.

Image fusion using a multi-level image decomposition and fusion method.

In recent years, image fusion has emerged as an important research field due to its various applications. Images acquired by different sensors have significant differences in feature representation due to the different imaging principles. Taking visible and infrared image fusion as an example, visible images contain abundant texture details with high spatial resolution. In contrast, infrared images can obtain clear target contour information according to the principle of thermal radiation, and work well in all day/night and all weather conditions. Most existing methods employ the same feature extraction algorithm to get the feature information from visible and infrared images, ignoring the differences among these images. Thus, this paper proposes what we believe to be a novel fusion method based on a multi-level image decomposition method and deep learning fusion strategy for multi-type images. In image decomposition, we not only utilize a multi-level extended approximate low-rank projection matrix learning decomposition method to extract salient feature information from both visible and infrared images, but also apply a multi-level guide filter decomposition method to obtain texture information in visible images. In image fusion, a novel fusion strategy based on a pretrained ResNet50 network is presented to fuse multi-level feature information from both visible and infrared images into corresponding multi-level fused feature information, so as to improve the quality of the final fused image. The proposed method is evaluated subjectively and objectively in a large number of experiments. The experimental results demonstrate that the proposed method exhibits better fusion performance than other existing methods.

Multi-Object Tracking Algorithm for RGB-D Images Based on Asymmetric Dual Siamese Networks.

Currently, intelligent security systems are widely deployed in indoor buildings to ensure the safety of people in shopping malls, banks, train stations, and other indoor buildings. Multi-Object Tracking (MOT), as an important component of intelligent security systems, has received much attention from many researchers in recent years. However, existing multi-objective tracking algorithms still suffer from trajectory drift and interruption problems in crowded scenes, which cannot provide valuable data for managers. In order to solve the above problems, this paper proposes a Multi-Object Tracking algorithm for RGB-D images based on Asymmetric Dual Siamese networks (ADSiamMOT-RGBD). This algorithm combines appearance information from RGB images and target contour information from depth images. Furthermore, the attention module is applied to repress the redundant information in the combined features to overcome the trajectory drift problem. We also propose a trajectory analysis module, which analyzes whether the head movement trajectory is correct in combination with time-context information. It reduces the number of human error trajectories. The experimental results show that the proposed method in this paper has better tracking quality on the MICC, EPFL, and UMdatasets than the previous work.

Superresolution of Radar Forward-Looking Imaging Based on Accelerated TV-Sparse Method

Total variation-sparse (TV-sparse)-based multiconstraint devonvolution method has been used to realize superresolution imaging and preserve target contour information simultaneously of radar forward-looking imaging. However, due to the existence of matrix inversion, it suffers from high computational complexity, which restricts the ability of radar real-time imaging. In this article, an Gohberg-Semencul (GS) decomposition-based fast TV-sparse (FTV-sparse) method is proposed to reduce the computational complexity of TV-sparse method. The acceleration strategy utilizes the low displacement rank features of Toeplitz matrix, realizing fast matrix inversion by using a GS representation. It reduces the computational complexity of traditional TV-sparse method from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), benefiting for improvement of the computing efficiency. The simulation and experimental data processing results show that the proposed FTV-sparse method has almost no resolution loss compared with the traditional TV sparse method. Hardware test results show that the proposed FTV-sparse method significantly improves the computational efficiency of TVsparse method.

TV-Sparse Super-Resolution Method for Radar Forward-Looking Imaging

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

Multi-scale contour detection model based on fixational eye movement mechanism

Physiological evidence has shown that classical receptive field (CRF) responses in the primary visual cortex (V1) can be suppressed by its surrounding region, called the non-classical receptive field (nCRF). Currently, the contour detection model based on the physiological characteristics of the V1 region is mainly used to suppress texture and highlight contour information through the inhibition of nCRF features. However, the effect of eye movement on inhibition is not considered in the inhibition calculation of such models. Inspired by the fixational eye movement (FEyeM) mechanism, we propose a multi-scale contour detection model based on fixational eye movement (MsFem) and the surrounding suppression mechanism. A bank of filters was proposed to simulate the influence of FEyeMs on nCRF, and multi-scale cues were utilized to improve the fine and coarse contour extraction and texture inhibition. The experiments showed that MsFem outperformed some biologically motivated ones in retaining the small-scale target contour information and suppressing the large-scale background textures.

1P1-D04 CT-USの相補利用による体内組織の実時間トラッキング手法の開発

In this study, we developed a method of real-time tracking of tissue contour with a relatively low exposure by use of Computed Tomography (CT) and Ultrasonic (US) Image. Even if it is difficult to extract tissue contour in US image, the proposed method made real-time tracking of tissue contour possible by complementary usage the contour information in CT image and the remaining motion information in US image. Specifically, the identified target contour information in CT image was traced onto the corresponding position in US image. The motion vector of the target tissue contours was estimated by speckle tracking of its surroundings. The target tissue contours in the CT image was animated by using the motion vector. The results of the evaluation test shown the possibility of real-time tissues tracking by the proposed method.

Location Technology of Correlation Peaks Based on Joint Transform Correlator

By comparing the similarity of their contour information, hybrid photoelectric joint transform correlator (JTC) is used to recognize the specific target. An effective method to increase the recognition rate is to extract the contour information of target and template and decrease the noise interference in image background. The principle of joint transform correlator is given in this paper. To increase the recognizing rate, a mask algorithm is presented, with which the coordinate of the correlation peaks can be located precisely. Before the mask algorithm, edge extraction with Robert operator is carried out to enhance the target contour information and reduce the background noise. Amounts of experiments prove the mask algorithm can locate the correlation peaks precisely. As an example, an experimental result is given with a car as the detected target. The coordinate of this car is (362, 438). Experimental results confirm the feasibility and effectiveness of the mask algorithm.

Aggressive motion detection based on normalised Radon transform and online AdaBoost

A framework for human aggressive motion detection in image sequences captured from a single stationary camera is described. Background subtraction, connected chips linking and mean-shift estimation are used to extract target contour information. The binary rectangle containing contour points of the blob-set is transformed into a normalised Radon matrix. With labelled (normal or aggressive motion) Radon matrix as feature pools an online AdaBoost feature selection is implemented. Using the selected classifiers the human aggressive motion in a frame can be recognised. Experimental results show that the system performs well.