|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | A correlative classifiers approach based on particle filter and sample set for tracking occluded target显示文摘Target tracking is one of the most important issues in computer vision and has been applied in many fields of science, engineering and industry. Because of the occlusion during tracking, typical approaches with single classifier learn much of occluding background information which results in the decrease of tracking performance, and eventually lead to the failure of the tracking algorithm. This paper presents a new correlative classifiers approach to address the above problem. Our idea is to derive a group of correlative classifiers based on sample set method. Then we propose strategy to establish the classifiers and to query the suitable classifiers for the next frame tracking. In order to deal with nonlinear problem, particle filter is adopted and integrated with sample set method. For choosing the target from candidate particles, we define a similarity measurement between particles and sample set. The proposed sample set method includes the following steps. First, we cropped positive samples set around the target and negative samples set far away from the target. Second, we extracted average Haarlike feature from these samples and calculate their statistical characteristic which represents the target model. Third, we define the similarity measurement based on the statistical characteristic of these two sets to judge the similarity between candidate particles and target model. Finally,we choose the largest similarity score particle as the target in the new frame. A number of experiments show the robustness and efficiency of the proposed approach when compared with other state-of-the-art trackers. | LI Kang HE Fa-zhi YU Hai-ping CHEN Xiao | 2017 | Applied Mathematics(A Journal of Chinese Universities)2017，32，3： | 6 |
| 2 | Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision显示文摘A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type Ⅴgroove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently. | De Xu, Min Tan, Xiaoguang Zhao, Zhiguo Tu Laboratory of Complex Systems and Intelligence Science, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, PRC | 2004 | International Journal of Automation and computing2004，1，1： | 5 |
| 3 | Radar maneuvering target tracking algorithm based on human cognition mechanism显示文摘Radar Maneuvering Targets Tracking(RMTT) in clutter is a quite challenging issue due to the errors in the models and the varying dynamics of the processes. Modern radar tracking system calls for the adaptive signal and data processing algorithm urgently to adapt the uncertainty of the environment. The mechanism of human cognition can help persons cope with the similar diffi-culties in visual tracking. Inspired by human cognition mechanism, a comprehensive method for RMTT is proposed. In the method, the model transition probability in Interacting Multiple Model(IMM) and the validation gate can be adjusted dynamically with target maneuver;the waveform in radar transmitter can vary with the perception of the environment. Experimental results in cluttered scenes show that the proposed algorithm is more accurate for perceiving the environment and targets, and the waveform selection algorithm is better than that with fixed waveform. | Shuliang WANG Daping BI Huailin RUAN Mingyang DU | 2019 | Chinese Journal of Aeronautics2019，32，7： | 4 |
| 4 | Real-time visual tracking using complementary kernel support correlation filters显示文摘Despite demonstrated success of SVM based trackers,their performance remains a boosting room if carefully considering the following factors:first,the tradeoff between sampling and budgeting samples affects tracking accuracy and efficiency much;second,how to effectively fuse different types of features to learn a robust target representation plays a key role in tracking accuracy.In this paper,we propose a novel SVM based tracking method that handles the first factor with the help of the circulant structures of the samples and the second one by a multi-kernel learning mechanism.Specifically,we formulate an SVM classification model for visual tracking that incorporates two types of kernels whose matrices are circulant,fully taking advantage of the complementary traits of the color and HOG features to learn a robust target representation.Moreover,it is fortunate that the SVM model has a closed-form solution in terms of both the classifier weights and the kernel weights,and both can be efficiently computed via fast Fourier transforms(FFTs).Extensive evaluations on OTB100 and VOT2016 visual tracking benchmarks demonstrate that the proposed method achieves a favorable performance against various state-of-the-art trackers with a speed of 50 fps on a single CPU. | Zhenyang SU Jing LI Jun CHANG Bo DU Yafu XIAO | 2020 | Frontiers of Computer Science2020，14，2： | 2 |
| 5 | Smooth-optimal Adaptive Trajectory Tracking Using an Uncalibrated Fish-eye Camera显示文摘This paper presents a two-stage smooth-optimal trajectory tracking strategy.Different from existing methods,the optimal trajectory tracked point can be directly determined in an uncalibrated fish-eye image.In the first stage,an adaptive trajectory tracking controller is employed to drive the tracking error and the estimated error to an arbitrarily small neighborhood of zero.Afterwards,an online smooth-optimal trajectory tracking planner is proposed,which determines the tracked point that can be used to realize smooth motion control of the mobile robot.The tracked point in the uncalibrated image can be determined by minimizing a utility function that consists of both the velocity change and the sum of cross-track errors.The performance of our planner is compared with other tracked point determining methods in experiments by tracking a circular trajectory and an irregular trajectory.Experimental results show that our method has a good performance in both tracking accuracy and motion smoothness. | Zhao-Bing Kang Wei Zou Zheng Zhu Hong-Xuan Ma | 2020 | International Journal of Automation and computing2020，17，2： | 1 |
| 6 | Robust feature learning for online discriminative tracking without large-scale pre-training显示文摘Owing to the inherent lack of training data in visual tracking,recent work in deep learning-based trackers has focused on learning a generic representation ofltine from large-scale training data and transferring the pre-trained feature representation to a tracking task.Offline pre-training is time-consuming,and the learned generic representation may be either less discriminative for tracking specific objects or overfitted to typical tracking datasets.In this paper, we propose an online discriminative tracking method based on robust feature learning without large-scale pre-training. Specifically,we first design a PCA filter bank-based convolutional neural network (CNN)architecture to learn robust features online with a few positive and negative samples in the high-dimensional feature space.Then,we use a simple softthresholding method to produce sparse features that are more robust to target appearance variations.Moreover,we increase the reliability of our tracker using edge information generated from edge box proposals during the process of visual tracking.Finally,effective visual tracking results are achieved by systematically combining the tracking information and edge box-based scores in a particle filtering framework.Extensive results on the widely used online tracking benchmark (OTB-50)with 50videos validate the robustness and effectiveness of the proposed tracker without large-scale pre-training. | Jun ZHANG Bineng ZHONG Pengfei WANG Cheng WANG Jixiang DU | 2018 | Frontiers of Computer Science2018，12，6： | 1 |
| 7 | Real-time manifold regularized context-aware correlation tracking显示文摘Despite the demonstrated success of numerous correlation filter(CF)based tracking approaches,their assumption of circulant structure of samples introduces significant redundancy to learn an effective classifier.In this paper,we develop a fast manifold regularized context-aware correlation tracking algorithm that mines the local manifold structure information of different types of samples.First,different from the traditional CF based tracking that only uses one base sample,we employ a set of contextual samples near to the base sample,and impose a manifold structure assumption on them.Afterwards,to take into account the manifold structure among these samples,we introduce a linear graph Laplacian regularized term into the objective of CF learning.Fortunately,the optimization can be efficiently solved in a closed form with fast Fourier transforms(FFTs),which contributes to a highly efficient implementation.Extensive evaluations on the OTB100 and VOT2016 datasets demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms in terms of accuracy and robustness.Especially,our tracker is able to run in real-time with 28 fps on a single CPU. | Jiaqing FAN Huihui SONG Kaihua ZHANG Qingshan LIU Fei YAN Wei LIAN | 2020 | Frontiers of Computer Science2020，14，2： | 1 |
| 8 | Visual tracking using discriminative representation with l2 regularization显示文摘In this paper,we propose a novel visual tracking method using a discriminative representation under a Bayesian framework.First,we exploit the histogram of gradient (HOG)to generate the texture features of the target templates and candidates.Second,we introduce a novel discriminative representation and l2-regularized least squares method to solve the proposed representation model.The proposed model has a closed-form solution and very high computational efficiency.Third,a novel likelihood function and an update scheme considering the occlusion factor are adopted to improve the tracking performance of our proposed method. Both qualitative and quantitative evaluations on 15 challenging video sequences demonstrate that our method can achieve more robust tracking results in terms of the overlap rate and center location error. | Haijun WANG Hongjuan GE | 2019 | Frontiers of Computer Science2019，13，1： | 0 |
| 9 | Road Surface Condition and Monitoring System Utilizing Motorcycle(ROCOM)—System Development,Validation and Field Test显示文摘Motorcycles are the riskiest mode of travel in Malaysia,however motorcycles are also very sensitive to the road surface condition.Thus,by taking advantage of this,a system of software applications that analyses motorcycle motion and mapped out risky road sections was developed,i.e.ROCOM.The system consists of three major components,i.e.ROCOM Data Logger app,which utilizes a smart phone to collect acceleration data,ROCOM Risk Mapping app,which is a web-based application,and ROCOM Visual Tracking,which is a stand along software.ROCOM is able to detect adverse acceleration( >2g)or vibration on the road surface similar to the High Accuracy GPS Data Logging for Vehicle Testing(VBOX).Risk mapping validation along a section of the motorcycle lane along Federal Route 2 shows that not only ROCOM have the similar risk-mapping pattern,but its route tracking capability on the map is far superior that the VBOX.The pilot and field test results showed that ROCOM works best when mounting the smartphone on the motorcycle handle bar or basket,and it can detect various road anomalies with successful detection rate of 62%,with high detection rate when passing through uneven road surfaces. | Muhammad Marizwan Abdul Manan Muhammad Ruhaizat Abd Ghani | 2018 | Journal of Traffic and Transportation Engineering2018，6，6： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员