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Object Tracking in Crowded Video Scenes Based on the Undecimated Wavelet Features and Texture Analysis  [cached]
M. Khansari,H. R. Rabiee,M. Asadi,M. Ghanbari
EURASIP Journal on Advances in Signal Processing , 2008, DOI: 10.1155/2008/243534
Abstract: We propose a new algorithm for object tracking in crowded video scenes by exploiting the properties of undecimated wavelet packet transform (UWPT) and interframe texture analysis. The algorithm is initialized by the user through specifying a region around the object of interest at the reference frame. Then, coefficients of the UWPT of the region are used to construct a feature vector (FV) for every pixel in that region. Optimal search for the best match is then performed by using the generated FVs inside an adaptive search window. Adaptation of the search window is achieved by interframe texture analysis to find the direction and speed of the object motion. This temporal texture analysis also assists in tracking of the object under partial or short-term full occlusion. Moreover, the tracking algorithm is robust to Gaussian and quantization noise processes. Experimental results show that the proposed algorithm has good performance for object tracking in crowded scenes on stairs, in airports, or at train stations in the presence of object translation, rotation, small scaling, and occlusion.
Precise multiple object identification and tracking using efficient visual attributes in dense crowded scene with regions of rational movement
Pushpa D,H. S. Sheshadri
International Journal of Computer Science Issues , 2012,
Abstract: The proposed model represents a unique technique for detection and tracking multiple objects from a dense cluttered area like crowd by deploying greed algorithm. Understanding the complexity of deploying various image attributes e.g. edge, color etc, the proposed system will illustrate cost effective and robust procedure of using low-level attributes which takes very less computational time in order to produce autonomous rational mobility region as resultant. The technique also considers various difficult real-time scenarios in the dense crowd in order to design a highly cost effective algorithm. Performance analysis is carried out with different set of video sequences to find that proposed system has gradual robust detection rate as well as highly cost-effective computationally.
A Novel Robust Tracking Algorithm in Cluttered Environments for Distributed Sensor Network  [PDF]
Yunting Liu,Yuanwei Jing,Siying Zhang,Hui Guo
International Journal of Distributed Sensor Networks , 2013, DOI: 10.1155/2013/384318
Abstract: Tracking has attracted much attention over the past few years, particularly in the field of distributed sensor network. The most challenging issue is nonline of sight (NLOS) problem in cluttered environments such as indoor or urban areas since the presence of NLOS errors leads to severe degradation in the tracking performance. In this paper, we propose a novel robust tracking algorithm to mitigate the measurement noise and NLOS error. The robust localization method is firstly employed to estimate the positions of the mobile node with different subgroups. Then the residual test method is used to remove the larger localization error. Finally, the modified Kalman filter is introduced to improve the tracking accuracy. Simulation results show that the proposed algorithm can track the mobile node and estimate the position with relatively higher accuracy in comparison with existing methods. 1. Introduction Due to the rapid development of the distributed sensing and wireless communication technologies, the distributed sensor network has emerged as a promising solution for a wide range of applications, such as habitat monitoring, energy management, and military initiatives [1]. The sensor node has the ability to collect, process, and store measurement information, as well as to communicate with other nodes via the wireless communication. Tracking technologies, which are designated to estimate the trajectory of a mobile object (or mobile node), have attracted much attention in recent years because of the increasing demand on location based services [2]. The tracking schemes estimate the position of the mobile object based on measured signals from the beacon nodes. A number of tracking measurement methods have been widely studied with various types of signal measurements, including time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), and received signal strength (RSS) [3–5]. TOA measurement method relies on the travel time of signal between mobile node and beacon nodes, but this method is affected by the synchronization error seriously. TDOA method measures the signals’ arrival time difference between beacon nodes and mobile node. This method needs extra hardware configuration. As an inexpensive approach, RSS has established the mathematical model on the basis of path loss attenuation with distance. AOA method depends on the direction of propagation of a radio frequency wave incident on the antenna array. In this paper, any of the ranging measurements can be used in the proposed algorithm. If the line of sight (LOS) propagation
An On-line Variational Bayesian Model for Multi-Person Tracking from Cluttered Scenes  [PDF]
Sileye Ba,Xavier Alameda-Pineda,Alessio Xompero,Radu Horaud
Statistics , 2015,
Abstract: Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thorougly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions: A Bayesian framework for tracking an unknown and varying number of persons, a variational expectation-maximization (VEM) algorithm with closed-form expressions for the posterior distributions and model parameter estimation, A method capable of exploiting observations from multiple detectors, thus enabling multimodal fusion, and built-in object-birth and object-visibility processes that allow to handle person appearance and disappearance. The method is evaluated on standard datasets, which shows competitive and encouraging results with respect to state of the art methods.
Momentum Based Level Set Method For Accurate Object Tracking  [cached]
Haocheng Le,Linglong Hu,Yuanjing Feng
International Journal of Intelligent Systems and Applications , 2010,
Abstract: This paper proposes a novel object tracking method that is robust to a cluttered background and large motion. First, a posterior probability measure (PPM) is adopted to locate the object region. Then the momentumbased level set is used to evolve the object contour in order to improve the tracking precision. To achieve rough object localization, the initial target position is predicted and evaluated by the Kalman filter and the PPM, respectively. In the contour evolution stage, the active contour is evolved on the basis of an object feature image. This method can acquire more accurate target template as well as target center. The comparison between our method and the kernelbased method demonstrates that our method can effectively cope with the deformation of object contour and the influence of the complex background when similar colors exist nearby. Experimental results show that our method has higher tracking precision.
Tracking of a Moving Target by Improved Potential Field Controller in Cluttered Environments
Marwa Taher Yousef,Hosam Eldin Ibrahim Ali,Shahira Mahmoud Habashy,Elsayed Mostafa Saad
International Journal of Computer Science Issues , 2012,
Abstract: In this paper, robot tracking of a moving target in cluttered environments by using an improved potential field controller is proposed. Genetic algorithms are used to improve the potential field controller by optimizing the forces applied to the robot. This improvement makes the robot path much more smoother during the tracking. A measure of smoothness is used to guide the genetic algorithm during the optimization. Of course more smoothing gives less distance and more speed to reach the goal. The optimized controller is simulated with different cases on Windows Vista using Matlab Software. These cases include environments with single obstacle up to three obstacles and multi-knee corridor. Results are compared to previous work, illustrating the superiority of the proposed work. Tracking of a moving target in the same cases are also simulated.
Methods of Object Tracking
Gabriel DANCIU,Iuliu SZEKELY
Annals of Dunarea de Jos , 2009,
Abstract: This paper presents two methods of object detection/tracking in a video. Thefirst method will use the motion tracking technique combined with density detectionwhich will be described. The second one is based on the SURF algorithm.
Rebound of Region of Interest (RROI), a New Kernel-Based Algorithm for Video Object Tracking Applications  [PDF]
Andres Alarcon Ramirez, Mohamed Chouikha
Journal of Signal and Information Processing (JSIP) , 2014, DOI: 10.4236/jsip.2014.54012
Abstract: This paper presents a new kernel-based algorithm for video object tracking called rebound of region of interest (RROI). The novel algorithm uses a rectangle-shaped section as region of interest (ROI) to represent and track specific objects in videos. The proposed algorithm is constituted by two stages. The first stage seeks to determine the direction of the object’s motion by analyzing the changing regions around the object being tracked between two consecutive frames. Once the direction of the object’s motion has been predicted, it is initialized an iterative process that seeks to minimize a function of dissimilarity in order to find the location of the object being tracked in the next frame. The main advantage of the proposed algorithm is that, unlike existing kernel-based methods, it is immune to highly cluttered conditions. The results obtained by the proposed algorithm show that the tracking process was successfully carried out for a set of color videos with different challenging conditions such as occlusion, illumination changes, cluttered conditions, and object scale changes.
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.
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.
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