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Multiple Maneuvering Target tracking by Improved Particle Filter Based on Multiscan JPDA

MATHEMATICAL PROBLEMS IN ENGINEERING 2012年第1期2012卷 1-25页

作者： Liu, Jing Han, ChongZhao Han, Feng Hu, Yu Xi An Jiao Tong Univ MOE Key Lab Intelligent & Networked Syst Inst Integrated Automat Sch Elect & Informat Engn Xian 710049 Shaanxi Provinc Peoples R China Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Provinc Peoples R China

The multiple maneuvering target tracking algorithm based on a particle filter is addressed. The equivalent-noise approach is adopted, which uses a simple dynamic model consisting of target state and equivalent noise which accounts for the combined effects of the process noise and maneuvers. The equivalent-noise approach converts the problem of maneuvering target tracking to that of state estimation in the presence of nonstationary process noise with unknown statistics. A novel method for identifying the nonstationary process noise is proposed in the particle filter framework. Furthermore, a particle filter based multiscan Joint Probability Data Association (JPDA) filter is proposed to deal with the data association problem in a multiple maneuvering target tracking. In the proposed multiscan JPDA algorithm, the distributions of interest are the marginal filtering distributions for each of the targets, and these distributions are approximated with particles. The multiscan JPDA algorithm examines the joint association events in a multiscan sliding window and calculates the marginal posterior probability based on the multiscan joint association events. The proposed algorithm is illustrated via an example involving the tracking of two highly maneuvering, at times closely spaced and crossed, targets, based on resolved measurements.

关键词： tracking algorithms DYNAMIC models MONTE Carlo method PROBABILITY theory algorithms

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Markov chain Monte Carlo based video tracking algorithm

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PROGRAMMING AND COMPUTER SOFTWARE 2017年第4期43卷 224-229页

作者： Kuplyakov, D. Shalnov, E. Konushin, A. Moscow MV Lomonosov State Univ Dept Computat Math & Cybernet Moscow 119991 Russia NRU Higher Sch Econ Moscow 101000 Russia

The paper considers a problem of multiple person tracking. We present the algorithm to automatic people tracking on surveillance videos recorded by static cameras. Proposed algorithm is an extension of approach based on tracking-by-detection of people heads and data association using Markov chain Monte Carlo (MCMC). Short track fragments (tracklets) are built by local tracking of people heads. Tracklet postprocessing and accurate results interpolation were shown to reduce number of false positives. We use position deviations of tracklets and revised entry/exit points factor to separate pedestrians from false positives. The paper presents a new method to estimate body position, that increases precision of tracker. Finally, we switched HOG-based detector to cascade one. Our evaluation shows proposed modifications significantly increase tracking quality.

关键词： MARKOV chain Monte Carlo DIGITAL video tracking algorithms VIDEO surveillance DIGITAL cameras

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Adaptive Sensor Activation Algorithm for Target tracking in Wireless Sensor Networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2012年第6期8卷 1-10页

作者： Zhou, Wei Shi, Weiren Wang, Xiaogang Wang, Kai Chongqing Univ Coll Automat Chongqing 400030 Peoples R China

Target tracking is an important application of wireless sensor networks where energy conservation plays an important role. In this paper, we propose an energy-efficient sensor activation protocol based on predicted region technique, called predicted region sensor activation algorithm (PRSA). The proposed algorithm predicts the moving region of target in the next time interval instead of predicting the accurate position, by analyzing current location and velocity of the target. We take these nodes within the predicted region as waiting-activation nodes and establish activation strategy. The fewest essential number of sensor nodes within the predicted region will be activated to monitor the target. Thus, the number of nodes that was involved in tracking the target will be decreased to save energy and prolong the network's operational lifetime. The simulation results demonstrate the effectiveness of the proposed algorithm.

关键词： tracking algorithms WIRELESS sensor networks ENERGY conservation ENERGY consumption COMPUTER network protocols PREDICTION models

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Bayesian Approach to Extended Object and Cluster tracking using Random Matrices

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2008年第3期44卷 1042-1059页

作者： Koch, Johann Wolfgang FGAN FKIE D-53343 Wachtberg Germany

In algorithms for tracking and sensor data fusion the targets to be observed are usually considered as point source objects;i.e., compared with the sensor resolution their extension is neglected. Due to the increasing resolution capabilities of modem sensors, however, this assumption is often no longer valid as different scattering centers of an object can cause distinct detections when passing the signal processing chain. Examples of extended targets are found in short-range applications (littoral surveillance, autonomous weapons, or robotics). A collectively moving target group can also be considered as an extended target. This point of view is the more appropriate, the smaller the mutual distances between the individual targets are. Due to the resulting data association and resolution conflicts any attempt of tracking the individual objects within the group seems to be no longer reasonable. With simulated sensor data produced by a partly unresolvable aircraft formation the addressed phenomena are illustrated and an approximate Bayesian solution to the resulting tracking problem is proposed. Ellipsoidal object extensions are modeled by random matrices, which are treated as additional state variables to be estimated or tracked. We expect that the resulting tracking algorithms are also relevant for tracking large, collectively moving target swarms.

关键词： Bayes methods filtering theory matrix algebra pattern clustering random processes sensor fusion tracking Bayes method cluster tracking object extension random matrix sensor data fusion tracking algorithms

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Visual tracking Using an Insect Vision Embedded Particle Filter

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-16页

作者： Guo, Wei Zhao, Qingjie Gu, Dongbing Beijing Inst Technol Sch Comp Sci Beijing Key Lab Intelligence Informat Technol Beijing 100081 Peoples R China Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England

Particle filtering (PF) based object tracking algorithms have drawn great attention from lots of scholars. The core of PF is to predict the possible location of the target via the state transition model. One commonly adopted approach is resorting to prior motion cues under the smooth motion assumption, which performs well when the target moves with a relatively stable velocity. However, it would possibly fail if the target is undergoing abrupt motion. To address this problem, inspired by insect vision, we propose a simple yet effective visual tracking framework based on PF. Utilizing the neuronal computational model of the insect vision, we estimate the motion of the target in a novel way so as to refine the position state of propagated particles using more accurate transition mode. Furthermore, we design a novel sample optimization framework where local and global search strategies are jointly used. In addition, we propose a new method to monitor long duration severe occlusion and we could recover the target. Experiments on publicly available benchmark video sequences demonstrate that the proposed tracking algorithm outperforms the state-of-the art methods in challenging scenarios, especially for tracking target which is undergoing abrupt motion or fast movement.

关键词： OBJECT tracking (Computer vision) tracking algorithms MONTE Carlo method COMPUTATIONAL complexity EXPERIMENTS

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Progress on the labeling and single-particle tracking technologies of viruses

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ANALYST 2014年第13期139卷 3336-3346页

作者： Huang, Li-Li Xie, Hai-Yan Beijing Inst Technol Sch Life Sci Beijing 100081 Peoples R China

Understanding and unravelling the invasion mechanisms of virus infection is of high importance for preventing and treating viral diseases. Single-virion tracking is a powerful way for exploring the mechanisms of viral infection. For successful tracking, the virus and cellular structures of interest must be fluorescently labeled;the microscope imaging technology must be sufficiently powerful for real-time single virion or viral component tracking. All fields of scientists have made great efforts and improvements. Here we will review the recent advances in virus labeling and the emerging fluorescence imaging technologies used in the imaging and tracking of viruses.

关键词： tracking algorithms VIRUSES FLUORESCENCE IMAGING systems VIRION

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Robust Visual Correlation tracking

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MATHEMATICAL PROBLEMS IN ENGINEERING 2015年第1期2015卷 1-13页

作者： Zhang, Lei Wang, Yanjie Sun, Honghai Yao, Zhijun He, Shuwen Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys Changchun 130033 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China North Automat Control Technol Res Inst Taiyuan 030006 Peoples R China

Recent years have seen greater interests in the tracking-by-detection methods in the visual object tracking, because of their excellent tracking performance. But most existing methods fix the scale which makes the trackers unreliable to handle large scale variations in complex scenes. In this paper, we decompose the tracking into target translation and scale prediction. We adopt a scale estimation approach based on the tracking-by-detection framework, develop a new model update scheme, and present a robust correlation tracking algorithm with discriminative correlation filters. The approach works by learning the translation and scale correlation filters. We obtain the target translation and scale by finding the maximum output response of the learned correlation filters and then online update the target models. Extensive experiments results on 12 challenging benchmark sequences show that the proposed tracking approach reduces the average center location error (CLE) by 6.8 pixels, significantly improves the performance by 17.5% in the average success rate (SR) and by 5.4% in the average distance precision (DP) compared to the second best one of the other five excellent existing tracking algorithms, and is robust to appearance variations introduced by scale variations, pose variations, illumination changes, partial occlusion, fast motion, rotation, and background clutter.

关键词： OBJECT tracking (Computer vision) ROBUST control tracking algorithms PROBLEM solving CORRELATION (Statistics)

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Unknown Clutter Estimation by FMM Approach in Multitarget tracking Algorithm

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MATHEMATICAL PROBLEMS IN ENGINEERING 2014年第1期2014卷 1-11页

作者： Lv, Ning Lian, Feng Han, Chongzhao Xi An Jiao Tong Univ Sch Elect & Informat Inst Integrated Automat MOE KLINNS Lab Xian 710049 Peoples R China Xian Res Inst Hitech Xian 710025 Peoples R China

Finite mixture model (FMM) approach is a research focus in multitarget tracking field. The clutter was treated as uniform distribution previously. Aiming at severe bias caused by unknown and complex clutter, a multitarget tracking algorithm based on clutter model estimation is put forward in this paper. Multitarget likelihood function is established with FMM. In this frame, the algorithms of expectation maximum (EM) and Markov Chain Monte Carlo (MCMC) are both consulted in FMM parameters estimation. Furthermore, target number and multitarget states can be estimated precisely after the clutter model fitted. Association between target and measurement can be avoided. Simulation proved that the proposed algorithm has a good performance in dealing with unknown and complex clutter.

关键词： tracking algorithms FINITE mixture models (Statistics) MARKOV chain Monte Carlo PARAMETER estimation LIKELIHOOD ratio tests SIMULATION methods & models

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A video-based object detection and tracking system for weight sensitive UAVs

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第7期78卷 9149-9167页

作者： Abughalieh, Karam M. Sababha, Belal H. Rawashdeh, Nathir A. Princess Sumaya Univ Technol Comp Engn Dept Amman 11941 Jordan German Jordanian Univ Dept Mechatron Engn Amman 11180 Jordan

The capability of detecting and tracking targets can play a significant role in mobile robot navigation systems. Visual tracking systems may control the direction and speed of motion of a robot to keep the target in its field of view either by moving the robot itself or the vision sensors. In this work, a compact size tracking and guiding system that could be mounted on weight-sensitive UAV platforms is presented. The system combines a motion detection technique that could be used with non-static cameras in addition to color filtering to detect and track objects in the field of view of a UAV. This hybrid system provides a reliable tracking system for low resolution images taken by a UAV camera. The proposed system implements keypoint detection algorithms including SIFT, SURF and FAST, a motion detection method using frame subtraction and object detection algorithms using color back projection in a hybrid approach that utilizes the best of each algorithm and avoids heavy usage of computing resources. Keypoint detectors SURF, SIFT and FAST are tested and implemented for the purpose of image alignment and frame subtractions. Experimental tests showed the system's ability to detect and track low detailed targets. The system is tested on a UAV using a Raspberry Pi 2 mini-computer running OpenCV libraries and was able to process eleven frames per second implementing object detection and tracking. The test objects were mainly cars monitored from different altitudes through a UAV downward pointing camera.

关键词： Computer vision Object tracking tracking algorithms Aerial video processing Keypoint detector Background subtraction UAV OpenCV

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Detecting Anomalies from a Multitarget tracking Output

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2014年第1期50卷 798-803页

作者： Ristic, Branko DSTO Melbourne Vic 3207 Australia

Surveillance systems typically incorporate multitarget tracking algorithms for sequential estimation of kinematic states (e.g. positions, velocities) of moving objects in the surveillance domain of interest. This letter proposes an algorithm for online detection of anomalies in the motion and the count of objects, using the output of a multiobject tracking algorithm. The surveillance area is partitioned by a square grid and the kinematic states that fall inside each cell of the grid are modelled by a Poisson point process. During the unsupervised learning phase, the parameters of the Poisson point process are estimated for each cell. The testing phase is performed sequentially by threshold detection at a specified level of significance. The performance of the algorithm is illustrated using the Automatic Identification System (AIS) dataset in the context of maritime surveillance.

关键词： tracking algorithms AUTONOMOUS vehicles KINEMATICS POISSON processes AUTOMATIC identification systems

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