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Object Tracking with Monte Carlo Methods

ZHANG Hai-qing,LI Hou-qiang,

中国图象图形学报 , 2008,
Abstract: For tracking object more robustly and rapidly, a novel approach for object tracking using Monte Carlo method is presented, which is motivated by the method of tracking with particle filter. In this approach, the locations and scales of the candidates in the next frame are sampled through Monte Carlo technique. Then the similitude degree between the samples and the reference objects are calculated. At last, the object states are estimated and the object under tracking is attained. This method does not need the motion information of the object, so it is especially good at tracking flexible object. The method is easier to implement than other present methods. Furthermore, it is proved to be quite robust and versatile by the numerical experiments.
Adaptive Objectness for Object Tracking  [PDF]
Pengpeng Liang,Chunyuan Liao,Xue Mei,Haibin Ling
Computer Science , 2015,
Abstract: Object tracking is a long standing problem in vision. While great efforts have been spent to improve tracking performance, a simple yet reliable prior knowledge is left unexploited: the target object in tracking must be an object other than non-object. The recently proposed and popularized objectness measure provides a natural way to model such prior in visual tracking. Thus motivated, in this paper we propose to adapt objectness for visual object tracking. Instead of directly applying an existing objectness measure that is generic and handles various objects and environments, we adapt it to be compatible to the specific tracking sequence and object. More specifically, we use the newly proposed BING objectness as the base, and then train an object-adaptive objectness for each tracking task. The training is implemented by using an adaptive support vector machine that integrates information from the specific tracking target into the BING measure. We emphasize that the benefit of the proposed adaptive objectness, named ADOBING, is generic. To show this, we combine ADOBING with seven top performed trackers in recent evaluations. We run the ADOBING-enhanced trackers with their base trackers on two popular benchmarks, the CVPR2013 benchmark (50 sequences) and the Princeton Tracking Benchmark (100 sequences). On both benchmarks, our methods not only consistently improve the base trackers, but also achieve the best known performances. Noting that the way we integrate objectness in visual tracking is generic and straightforward, we expect even more improvement by using tracker-specific objectness.
Online Object Tracking with Proposal Selection  [PDF]
Yang Hua,Karteek Alahari,Cordelia Schmid
Computer Science , 2015,
Abstract: Tracking-by-detection approaches are some of the most successful object trackers in recent years. Their success is largely determined by the detector model they learn initially and then update over time. However, under challenging conditions where an object can undergo transformations, e.g., severe rotation, these methods are found to be lacking. In this paper, we address this problem by formulating it as a proposal selection task and making two contributions. The first one is introducing novel proposals estimated from the geometric transformations undergone by the object, and building a rich candidate set for predicting the object location. The second one is devising a novel selection strategy using multiple cues, i.e., detection score and edgeness score computed from state-of-the-art object edges and motion boundaries. We extensively evaluate our approach on the visual object tracking 2014 challenge and online tracking benchmark datasets, and show the best performance.
Survey of moving object tracking algorithm

ZHANG Juan,MAO Xiao-bo,CHEN Tie-jun,

计算机应用研究 , 2009,
Abstract: This paper divided the issue of moving object tracking into moving detection and object tracking separately. First, introduced the research status, methods and common algorithms. Second, by means of comparing the advantages and disadvantages of various methods, discussed the technical difficulties they faced. Finally, prospected some future directions of object tracking method.
A Survey on Moving Object Detection and Tracking in Video Surveillance System  [PDF]
Kinjal A Joshi,Darshak G. Thakore
International Journal of Soft Computing & Engineering , 2012,
Abstract: This paper presents a survey of various techniques related to video surveillance system improving the security. The goal of this paper is to review of various moving object detection and object tracking methods. This paper focuses on detection of moving objects in video surveillance system then tracking the detected objects in the scene. Moving Object detection is first low level important task for any video surveillance application. Detection of moving object is a challenging task. Tracking is required in higher level applications that require the location and shape of object in every frame. In this survey, I described Background subtraction with alpha, statistical method, Eigen background Subtraction and Temporal frame differencing to detect moving object. I also described tracking method based on point tracking, kernel tracking and silhouette tracking.
A Tracking-Association Module-Based Study of Multi-Object Tracking Methods

ZHOU Liang
,LI Jing,YANG Fei

- , 2018, DOI: 10.13718/j.cnki.xdzk.2018.04.020
Abstract: 多目标跟踪面临的最大挑战是身份转换问题,其由目标物体间的相互遮挡造成.针对该问题,提出一种基于跟踪模型和关联模型的多目标跟踪方法.首先在跟踪模块针对每一个跟踪个体采用粒子滤波器还原其各自轨迹片段,并计算可信因子评估遮挡程度;然后在关联模块,将人体分割为头部、躯干和腿三部分,将人的面貌分为前侧和背侧两种,利用HSV颜色直方图方法提取各部分特征描述符,利用K最近邻方法探测个体之间的匹配程度,进行再次识别以实现轨迹片段的融合.实验结果表明,同传统的方法相比,提出的算法可有效避免由于遮挡引起的身份转换问题,且目标检测准确率有较大提高,检测准确率达到90.5%.
The most important challenge faced by algorithms designed for multi-object tracking is the identity switches due to occlusions and interactions between the same tracked objects. For the problem, the paper proposes a new multi-object tracking method based on the tracking module and the association module. Through the tracking module, the trajectory segments are recovered for each tracked individual based on the use of dedicated particle filters. In the association module, the body of the individual is segmented into three parts: heads, torso and legs, and his/her appearance is classified into two poses: front and back. Then, the HSV color histogram method is utilized to extract the feature descriptors of each part, and the K nearest neighbor algorithm is used to detect the match degree between individuals, which is re-identified to achieve the fusion of each trajectory segment. The experiment results demonstrate that compared with the traditional method, the proposed algorithm can effectively avoid the problem of identity transformation caused by occlusion, and achieve a detection accuracy as high as 90.5%
Particle Filter Algorithm for Object Tracking Based on Color Local Entropy  [PDF]
Huan Wang,Qinglin Wang,Yuan Li,Yaping Dai
Advances in Mechanical Engineering , 2013, DOI: 10.1155/2013/961019
Abstract: To achieve accurate visual object tracking and overcome the difficulties brought by the object deformation, occlusion, and illumination variations, a particle filter for object tracking algorithm based on color local entropy (CLE) is proposed. First we improved the traditional histogram weighted function by using a scale factor. Then, for the shortcoming that the color feature is sensitive to illumination and environmental interference, a color local entropy object observation model is constructed by mapping the object from color feature space to local entropy space. In addition, an adaptive updating strategy of the object template is designed and the number of particle is adjusted dynamically according to the tracking performance. The experimental results show that compared with several existing algorithms, the proposed algorithm is more effective and robust for the real-time object tracking under the situation of illumination variation, object occlusion, and nonlinear motion. 1. Introduction The real-time object tracking as the key step of Intelligent Transportation System (ITS) has been paid more and more attention. It is the premise of the traffic behavior analysis and prediction. Because of the complexity of the traffic environment, tracking moving object within a video sequence is still a challenging task. The difficulties of object tracking are mainly including the variation of object itself, such as pose variation in the process of the object motion and nonrigid object shape deformation; the extrinsic environment interference, such as illumination variation, occlusion, and camera vibration; and the real-time restriction. So far, many tracking approaches have been developed, such as Kalman filter [1], mean shift [2], and Particle filter (PF) [3, 4]. Among these methods, Particle filter can solve the problem of moving object tracking in nonlinear, non-Gaussian conditions. It has a wider range of applications than Kalman filter method. Since Isard and Blake [5] introduce it into the field of video based tracking, it has become an important research direction gradually. However, in the particle filter framework, the tracking performance depends greatly on the feature selection and expression. The color feature is stable and insensitive to object deformation, rotation, and partial occlusion, and it has advantages of simple calculation and fast processing, so it has been widely used in object tracking. In [6], Perez et al. proposed the color-based particle filter algorithm for object tracking and extension to multiple objects. Nummiaro et al. [7]
Neural Network for Object Tracking
M. Bouzenada,M.C. Batouche,Z. Telli
Information Technology Journal , 2007,
Abstract: Real-time object tracking is a problem which involves extraction and processing of critical information from complex and uncertain image data in a very short time. In this study, we present a global-based approach for object tracking in video images. Knowing grey level difference between target and estimated region containing the tracked object, we employ an Artificial Neural Network (ANN) to evaluate the corrective vector which is used to find the actual position of the target. Before, this ANN has been trained, during an offline stage, over a set of output and input samples to determine the relation between the intensity variations and position variations. The evaluation of the corrective vector can be obtained with small online computation and makes real-time implementation on standard workstations possible.
Object Tracking in Videos: Approaches and Issues  [PDF]
Duc Phu Chau,Fran?ois Bremond,Monique Thonnat
Computer Science , 2013,
Abstract: Mobile object tracking has an important role in the computer vision applications. In this paper, we use a tracked target-based taxonomy to present the object tracking algorithms. The tracked targets are divided into three categories: points of interest, appearance and silhouette of mobile objects. Advantages and limitations of the tracking approaches are also analyzed to find the future directions in the object tracking domain.
Tracking Moving Object with Structure Template

Xu Dong,Xu Wen-li,

电子与信息学报 , 2005,
Abstract: A two-step approach for tracking moving object is proposed in this paper. According of this approach, the structure template of object is firstly extracted with morphologic methods, and then the object tracking is performed with the structure template. The structure template is composed with the stable edges and the cross-points, which can describe the essential structure information of objects. The tracking processing can be divided into two steps: in the first step, the structure template is wholly moved very closed to the tracked object, and in the second step, the structure template is modified to converge to the cross-points and the edges in object image. Because of considering the structure information of object, the robusticity of tracking can be improved greatly.
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