• Title/Summary/Keyword: multiple target tracking

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Video-based Height Measurements of Multiple Moving Objects

  • Jiang, Mingxin;Wang, Hongyu;Qiu, Tianshuang
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.8 no.9
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    • pp.3196-3210
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    • 2014
  • This paper presents a novel video metrology approach based on robust tracking. From videos acquired by an uncalibrated stationary camera, the foreground likelihood map is obtained by using the Codebook background modeling algorithm, and the multiple moving objects are tracked by a combined tracking algorithm. Then, we compute vanishing line of the ground plane and the vertical vanishing point of the scene, and extract the head feature points and the feet feature points in each frame of video sequences. Finally, we apply a single view mensuration algorithm to each of the frames to obtain height measurements and fuse the multi-frame measurements using RANSAC algorithm. Compared with other popular methods, our proposed algorithm does not require calibrating the camera, and can track the multiple moving objects when occlusion occurs. Therefore, it reduces the complexity of calculation and improves the accuracy of measurement simultaneously. The experimental results demonstrate that our method is effective and robust to occlusion.

Visual Object Tracking by Using Multiple Random Walkers (다중 랜덤 워커를 이용한 객체 추적 기법)

  • Mun, Juhyeok;Kim, Han-Ul;Kim, Chang-Su
    • Journal of Broadcast Engineering
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    • v.21 no.6
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    • pp.913-919
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    • 2016
  • In this paper, we propose the visual tracking algorithm that takes advantage of multiple random walkers. We first show the tracking method based on support vector machine as [1] and suggest a method that suppresses feature vectors extracted from backgrounds while preserve features vectors from foregrounds. We also show how to discriminate between foregrounds and backgrounds. Learned by reducing influences of backgrounds, support vector machine can clearly distinguish foregrounds and backgrounds from the image whose target objects are similar to backgrounds and occluded by another object. Thus, the algorithm can track target objects well. Furthermore, we introduce a simple method improving tracking speed. Finally, experiments validate that proposed algorithm yield better performance than the state-of-the-art trackers on the widely-used benchmark dataset with high speed.

A Study of JPDA(Joint Probabilistic Data Association) to Decrease Track Coalescence & Switch in a Cluttered Environments (클러터 환경에서 Track Coalescence & Switch 감소를 위한 JPDA 기법연구)

  • Song, Dae-Buem
    • Journal of the Korea Institute of Military Science and Technology
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    • v.15 no.3
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    • pp.334-342
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    • 2012
  • Data association is important technology which designate final destination in the target tracking. The joint probabilistic data association(JPDA) algorithm provides excellent ability to maintain track on multiple targets. Currently, it is not easily implemented in real time because of track coalescence & switch. The aim of this paper is to develop probabilistic filters that increase JPDA's sensitivity and decrease track coalescence & switch in a cluttered environments.

An Adaptive Multiple Target Tracking Filter Using the EM Algorithm (EM 알고리즘을 이용한 적응다중표적추적필터)

  • Hong Jeong;Park, Jeong-Ho
    • Journal of the Institute of Electronics Engineers of Korea SP
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    • v.38 no.5
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    • pp.583-597
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    • 2001
  • Tracking the targets of interest has been one of the major research areas in radar surveillance system. We formulate the tracking problem as an incomplete data problem and apply the EM algorithm to obtain the MAP estimate. The resulting filter has a recursive structure analogous to the Kalman filter. The difference is that the measurement-update deals with multiple measurements and the parameter-update can estimate the system parameters. Through extensive experiments, it turns out that the proposed system is better than PDAF and NNF in tracking the targets. Also, the performance degrades gracefully as the disturbances become stronger.

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A Fuzzy-Neural network based IMM method for Tracking a Maneuvering Target (기동표적 추적을 위한 퍼지 뉴럴 네트워크 기반 다중모델 기법)

  • Son, Hyun-Seung;Joo, Young-Hoon;Park, Jin-Bae
    • Proceedings of the KIEE Conference
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    • 2006.07d
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    • pp.1858-1859
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    • 2006
  • This paper presents a new fuzzy-neural-network based interacting multiple model (FNNBIMM) algorithm for tracking a maneuvering target. To effectively handle the unknown target acceleration, this paper regards it as additional noise, time-varying variance to target model. Each sub model characterized by the variance of the overall process noise, which is obtained on the basis of each acceleration interval. Since it is hard to approximate this time-varying variance adaptively owing to the unknown acceleration, the FNN is utilized to precisely approximate this time-varying variance. The gradient descendant method is utilized to optimize each FNN. To show the feasibility of the proposed algorithm, a numerical example is provided.

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A Study on Fuzzy Interacting Multiple Model Algorithm for Maneuvering Target Tracking (기동 표적 추적을 위한 퍼지 IMM 알고리즘에 관한 연구)

  • Kim Hyun-Sik;Kim Jin-Soek;Hwang Soo-Bok
    • Journal of the Korea Institute of Military Science and Technology
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    • v.7 no.4 s.19
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    • pp.5-12
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    • 2004
  • The tracking algorithm based on the interacting multiple model(IMM) requires a considerable number of sub-models for the various maneuvering targets in order to have a good performance. But it is not feasible to use the nm algorithm in the real system because of the computational burden. Therefore, we need an algorithm which requires less computing resources while maintaining a good performance. In this paper, we propose a fuzzy interacting multiple model algorithm(FIMMA) for the tracking of maneuvering targets, which uses a minimal number of sub-models by considering the maneuvering properties and adjusts the mode transition probabilities by using the mode probability as a fuzzy input. In order to verify the performance of FIMMA, the developed algorithm is applied to the tracking of i borne targets. Simulation results show that the FIMMA is very effective in the tracking of maneuvering targets.

A Study on Multiple Target Tracking Using Self-Organizing Neural Network (자기조직화 신경망을 이용한 다중 표적 추적에 관한 연구)

  • 서창진;김광백
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.6
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    • pp.1304-1311
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    • 2003
  • Target tracking in a real world situation is difficult problem because of continuous variations in images, huge amounts of data, and high processing speed demands. The problem becomes even harder in the case of sea background. This paper presents an initial study of neural network based method for target detection and tracking in cluttering environment. The approach uses a combination of differential motion analysis, Kohonen self-organizing network and region growing method. The network is capable of detecting the mass-centers of moving objects within one frame. The history of neurons positions in the sequential frames approximates the traces of the targets. The experiments done with the network in simulated environment showed promising results.

Cooperative Standoff Tracking of a Moving Target using Decentralized Extended Information Filter (이동 목표물 협력추적을 위한 다수 무인항공기의 분산형 확장정보필터 설계)

  • Yoon, Seung-Ho;Bae, Jong-Hee;Kim, You-Dan
    • Journal of the Korean Society for Aeronautical & Space Sciences
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    • v.39 no.11
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    • pp.1013-1020
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    • 2011
  • This paper deals with the tracking problem of a moving target using multiple unmanned aerial vehicles. A decentralized extended information filter is designed to cooperatively estimate the position and the velocity of the moving target. The extended information filter is adopted to consider the range and the line-of-sight angle as measurement data. The decentralized scheme is applied to enhance the estimation performance using the information provided by other vehicles. Numerical simulation is performed to verify the tracking performance of the proposed decentralized filters.

Visual Object Tracking based on Particle Filters with Multiple Observation (다중 관측 모델을 적용한 입자 필터 기반 물체 추적)

  • Koh, Hyeung-Seong;Jo, Yong-Gun;Kang, Hoon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.14 no.5
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    • pp.539-544
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    • 2004
  • We investigate a visual object tracking algorithm based upon particle filters, namely CONDENSATION, in order to combine multiple observation models such as active contours of digitally subtracted image and the particle measurement of object color. The former is applied to matching the contour of the moving target and the latter is used to independently enhance the likelihood of tracking a particular color of the object. Particle filters are more efficient than any other tracking algorithms because the tracking mechanism follows Bayesian inference rule of conditional probability propagation. In the experimental results, it is demonstrated that the suggested contour tracking particle filters prove to be robust in the cluttered environment of robot vision.

Underwater target discrimination using geometry of ACM tracks (음향교란 항적의 기하학적 특성을 이용한 수중 표적 판별)

  • 정영헌;전상운;홍선목
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.3
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    • pp.110-119
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    • 1998
  • In this paper we discuss an algorithm to discriminate a garget under track against multiple acoustic counter-measure (ACM) sources, based on sequential testings of multiple hypotheses. The ACM sources are separated from the target under track and generate, while drifting, measurements with false range and Doppler information. The purpose of the ACM is to mislead the target tracking and to help the true target evade a pursuer. The proposed algorithm uses as a test statistic a function of the innovation sequences from extended Kalman filters to estimate the target dynamics and the drifting positions of the ACM sources. results of numerical experimenats are presented to show a performance profile of the proposed algorithm.

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