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The feature matching and tracking of binocular vision inter-frame images have been divided into three parts in this paper. Before image feature extraction, an image binarization segmentation method based on HSV color space is used to extract the target in order to reduce the searching range of feature points and improve the matching efficiency. About feature matching, a modified ORB method combining...
The mean shift algorithm (Mean Shift, MS) has been widely used because of the advantages of fewer iteration times and better real-time performance. In the other hand, because of the use of single color histogram representation of the target feature, the MS algorithm can't always track well in complex condition. Aiming at the problem that the traditional Mean-Shift algorithm is unstable when the background...
Visual tracking is a challenging problem in computer vision, especially when the camera platform is moving. To track a target pedestrian from a UAV (Unmanned Aerial Vehicle) mobile platform, multiple techniques, such as motion control of UAV, visual detection and tracking are needed. This study presents a new tracking method, which mainly involves object tracking, pedestrian detection and online color...
In order to accurately track sea cucumber on the assembly line to realize automatic grabbing using mechanical arm, an object tracking method based on Mean-shift algorithm was proposed. Firstly, the contours of the objects was extracted from the original image to select tracking target, and then the local image was cropped at the same position and local area in the second frame, and mean-shift algorithm...
Tracking of any given object forms integral part in surveillance, control and analysis applications. The video tracker presented here works on the principle of mean shift. Mean shift is an iterative algorithm which is extended to the field of object tracking. However mean shift tracker losses track of the object when there are variations in illumination. In order to improve the performance of the...
This paper addresses an efficient mean shift object tracking algorithm, by employing the joint color-texture histogram for object representation, and the mean shift algorithm for object tracking. The textural information of the object is extracted by using the four directional code called the local tetra pattern. The Ohta color model has been used for extracting color information. The local tetra...
Mean-shift tracking algorithm is a widely-used tool for efficiently tracking target. However, the background change and shade usually lead to tracking errors and low tracking accuracy. In this paper, we introduce a novel mean-shift tracking algorithm based on weighted sub-block which incorporates the improved level set target extraction. The weight of each sub-block is determined by the similarity...
Particle Filter is a Monte Carlo method that designed to approximate nonlinear problem. It usually used to track the target of the dynamic system only based on the color feature. The single characteristic is vulnerable to the impact on background noise, light, and some other factors. These would lead to misplace or lose the target. In order to improve the efficiency and robustness of the object tracking...
The object tracking by using single feature is possible to generate errors and easy to lose the target if the illumination and object size scale are changed. We propose a particle-filter-object-tracking algorithm. The proposed algorithm is based on a covariance region descriptor (CRD). The CRD can fuse different features of a targeted object region while handling various complex backgrounds. Hence,...
In this paper, we propose a novel online visual tracking algorithm using ensemble color feature. Visual tracking has long been one of the most important topics in computer vision. In recent years, significant progress has been achieved by the state-of-the-art tracking algorithms. However, some challenging problems still remain unsolved. One general problem is that it is difficult for a tracking algorithm...
Classical Camshift algorithm (CCA) has been widely used in the fields of visual tracking, image smoothing and segmentation currently. In view of the fact that CCA is easily affected by the object which is similar to the tracking target in hue, an improved Camshift algorithm is presented in this paper. In this algorithm, the histogram equalization is applied simultaneously to R, G, B channels in order...
The purpose of this paper is to analyze passengers' moving direction through the video shot in the entrances and exits of the subway stations. The results of the analysis will be helpful to relevant departments to manage the traffic condition, making a decision in the face of emergency. First of all, this paper adopts Haar features and Adaboost algorithm to implement the detection of human's head...
To reduce the tracking errors caused by high-speed motion and variable motion in the process of moving target tracking, a novel Mean-Shift tracking algorithm based on Kalman filter using adaptive window and sub-blocking is proposed in this paper. Moving target's utmost position is predicted by combining Kalman filter and historical information, which is used as the initial position. During describing...
Visual-attention-model (VAM) is a kind of model with good robustness of bionic vision. For ground object sensed on UAV (Unmanned Aerial Vehicle) platform, in this paper object detection and tracking algorithm based on VAM and extended Kaiman filter (EKF) is proposed, and applied to the ground surroundings for object detection and tracking. In order to quickly extract the ground objects in aerial images,...
Using a specific single feature in the tracking framework named cam shift for specific environments can effectively track the target. We compute log likelihood ratios of class conditional sample densities from object and background to creating weighted image. The two-class variance ratio is used to evaluating how well they separate sample distributions of object and background pixels. In experiments,...
Tracking failure is an inevitable problem in any object tracking algorithm. Online evaluation of a tracking algorithm to detect and correct failures is therefore an important task in any object tracking system. In this paper we propose an early tracking failure detection procedure for the Continuously Adaptive Mean-Shift(CAMShift) tracking algorithm. We also propose an algorithm to modify the tracker...
This paper presents the design flow of efficient and real-time object detection and tracking system in a complex environment. The design relies on color extraction, target recognition using neural networks and feature extraction. Furthermore, the system is designed to identify different objects and also to predict their movements. There are many domains where the detection and tracking of objects...
This paper presents a method for tracking a moving target by fusing bi-modal visual information from a deep infrared thermal imaging camera, and a conventional visible spectrum colour camera. The tracking method builds on well-known methods for colour-based tracking using particle filtering, but extends these to handle fusion of colour and thermal information when evaluating each particle. The key...
In Mean Shift algorithm, the features of the tracked target and the image matching similarity criterion have great influence on the result of tracking. a new algorithm of target tracking is proposed. the algorithm combine local binary pattern and color information to form a new feature CL, which tracks target by using a method of centroid iteration based on maximum posterior probability. Thanks to...
In this paper, we discuss theoretical foundations and a practical realization of real-time traffic sign detection and tracking method for a autonomous vehicle (P3-AT mobile robot). Continuous adaptive mean shift (Cam-Shift) algorithm is efficient for object tracking with its high speed and insensitiveness to the rotation and scale of the target, but it is influenced by the background. In order to...
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