Markov chain Monte Carlo data association for general multiple-target tracking problems

Oh, Songhwai; Russell, Stuart; Sastry, S. Shankar

doi:10.1109/cdc.2004.1428740

Cited by 95 publications

(7 citation statements)

References 36 publications

(63 reference statements)

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“…Even when a full probabilistic model is presented, the output of these algorithms may be a point estimate of the most likely track rather than a full posterior distribution over all possible tracks (Streit and Luginbuhl 1994;Gauvrit et al 1997). Oh et al (2004) propose a similar model to ours, along with a sampling strategy to explore the posterior space of data associations; however, their model does not place prior distributions over the genesis and lysis times. Also, their sampling strategy tends to be less effective when the feature vectors are spatially sparse relative to the scale of the storm dynamics, which is the case in our application.…”

Section: ) Jointly Solving the Initiation And Association Problemsmentioning

confidence: 93%

A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

Scharenbroich

Magnúsdóttir

Smyth

et al. 2010

Monthly Weather Review

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A probabilistic tracking model is introduced that identifies storm tracks from feature vectors that are extracted from meteorological analysis data. The model assumes that the genesis and lysis times of each track are unknown and estimates their values along with the track's position and storm intensity over time. A hidden-state dynamics model (Kalman filter) characterizes the temporal evolution of the storms.The model uses a Bayesian methodology for estimating the unknown lifetimes (genesis-lysis pairs) and tracks of the storms. Prior distributions are placed over the unknown parameters and their posterior distributions are estimated using a Markov Chain Monte Carlo (MCMC) sampling algorithm. The posterior distributions are used to identify and report the most likely storm tracks in the data. This approach provides a unified probabilistic framework that accounts for uncertainty in storm timing (genesis and lysis), storm location and intensity, and the feature detection process. Thus, issues such as missing observations can be accommodated in a statistical manner without human intervention.The model is applied to the field of relative vorticity at the 975-hPa level of analysis from the National Centers for Environmental Prediction Global Forecast System during May-October 2000-02, in the tropical east Pacific. Storm tracks in the National Hurricane Center best-track data (HURDAT) for the same period are used to assess the performance of the storm identification and tracking model.

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Section: ) Jointly Solving the Initiation And Association Problemsmentioning

confidence: 93%

A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

Scharenbroich

Magnúsdóttir

Smyth

et al. 2010

Monthly Weather Review

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“…Then, the MCMCDA searched for the best of matching pairs

falsefalse{false({BB}_{i}^{t}, {BB}_{j}^{normalt + 1} false) | i = 1, \dots, M, j = 1, \dots, N falsefalse}

. Please see [24] for details.…”

Section: Methodsmentioning

confidence: 99%

“…Then, we associated the object proposals at each frame with the ones at its consecutive frame, based on the Markov chain Monte Carlo data association technique (MCMCDA). The MCMCDA technique was proposed in [24] and has been widely utilised for multi-target visual tracking [25,26]. Recently, it was used to track a single target in severe background clutter, as presented in [1].…”

Section: Application: Visual Trackingmentioning

confidence: 99%

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Orthogonal object proposal and its application

Park

Kwon

2019

IET Computer Vision

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In this study, the authors propose an object proposal algorithm that can accurately propose object candidate regions at each frame, despite noise in a video. Accordingly, they define three orthogonal planes, namely vertical-horizontal, temporalvertical, and temporal-horizontal planes. As these planes are orthogonal, they are the most compact planes that can span the spatiotemporal space of a video. Their algorithm selects good object proposals for the vertical-horizontal plane with the help of the object proposal results of the other planes. Experimental results demonstrate that the proposed algorithm produces better object proposals than the baseline algorithm and other state-of-the-art methods. In particular, their method provides more accurate object proposals in challenging environments with severe noise and background clutter. In addition, the object proposal results are utilised for visual tracking problems, and the experimental results show that their visual tracker outperforms recent deep-learning-based trackers.

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“…Although the majority of data association algorithms, such as JPDA and MHT, take the peer-to-peer measurements and objectives into account, the Markov Chain Monte Carlo data association method does not work based on such hypotheses. In general, the Monte Carlo method is an approximate solution that considers the problem as a hybrid optimization problem and examines it through random space exploration, rather than enumerating all association options [26]. However, issues like long occlusions, severe video blur, sudden movements of the camera, and disruption of the state of targets can cause tracking failure.…”

Section: Literature Reviewmentioning

confidence: 99%

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Dadgar

Baleghi

Ezoji

2021

IJE

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In video surveillance, multiple objects tracking (MOT) is a challenging task due to object matching problem in consecutive frames. The present paper aims to propose an improved object matching approach in MOT based on Zernike moments and combination of multiple similarity distance metrics. In this work, the object is primarily detected using background subtraction method while the Gaussian Mixture Model (GMM) is applied for object extraction in the next frames. Subsequently, the color histogram and the magnitude of Zernike moments of the objects are calculated. In the next step, the objects are matched in the current and the previous frames based on the Hausdorff distance between objects, Earth Mover's distance (EMD) between their color histograms, and Chi-square distance between their Zernike moments. Then, a voting mechanism is designed to find the best consensus object matching from the aforementioned metrics. Eventually, the location of each object is predicted by the Kalman filter to continue tracking in subsequent frames. The results show that the object tracking and matching performance is improved using the proposed method in the video sequences of the multi-camera pedestrian "EPFL" video dataset. Specifically, errors caused by the merging of targets are reduced in the proposed tracking process.

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Markov chain Monte Carlo data association for general multiple-target tracking problems

Cited by 95 publications

References 36 publications

A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

A Bayesian Framework for Storm Tracking Using a Hidden-State Representation

Orthogonal object proposal and its application

Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

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