Privacy Enhancing Technologies

Fischer‐Hübner, Simone; Berthold, Stefan

doi:10.1007/978-3-642-03168-7

Cited by 9 publications

(2 citation statements)

References 25 publications

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“…In order to evaluate the performance of the proposed system for multiple human tracking, particularly to handle the situation of varying number of targets, close interactions and occlusions, we firstly chose sequences from three different publicly available video datasets: one from the PETS2009 dataset [36] where 3-6 human targets are walking in an outdoor campus environment, one sequence from the CAVIAR dataset [37] where 1-5 human targets are walking in a shopping mall environment and Background subtraction to extract the measurement set Z k for targets and the estimated positions of the new born targets. 5:…”

Section: A Dataset Selection and Parameter Setupmentioning

confidence: 99%

Social Force Model-Based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking

Feng

Wang

Dlay

et al. 2017

IEEE Trans. Multimedia

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Video-based multiple human tracking often involves several challenges, including target number variation, object occlusions, and noise corruption in sensor measurements. In this paper, we propose a novel method to address these challenges based on probability hypothesis density (PHD) filtering with a Markov chain Monte Carlo (MCMC) implementation. More specifically, a novel social force model (SFM) for describing the interaction between the targets is used to calculate the likelihood within the MCMC resampling step in the prediction step of the PHD filter, and a one class support vector machine (OCSVM) is then used in the update step to mitigate the noise in the measurements, where the SVM is trained with features from both color and oriented gradient histograms. The proposed method is evaluated and compared with state-of-the-art techniques using sequences from the CAVIAR, TUD, and PETS2009 datasets based on the mean Euclidean tracking error on each frame, the optimal subpattern assignment metric, and the multiple object tracking precision metric. The results show improved performance of the proposed method over the baseline algorithms, including the traditional particle PHD filtering method, the traditional SFM-based particle filtering method, multi-Bernoulli filtering, and an online-learningbased tracking method. Index Terms-Multiple human tracking, Markov chain Monte Carlo (MCMC) resampling, one class support vector machine (OCSVM), probable hypothesis density (PHD) filter, social force model. I. INTRODUCTIONV IDEO based multiple human tracking plays an important role in many applications such as surveillance, guidance, and homeland security, especially in enclosed environments such as an airport, campus or shopping mall. Tracking multiple human targets in the above situations presents several challenges including varying number of targets, object occlusion, and the adverse effect of environmental noise within measurements [1],

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Section: A Dataset Selection and Parameter Setupmentioning

confidence: 99%

Social Force Model-Based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking

Feng

Wang

Dlay

et al. 2017

IEEE Trans. Multimedia

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“…Measurements are utilized to calculate the adaptive weights, which are then used to enhance the tracking results. Simulations using sequences from the CAVIAR [13] and the PETS2009 [14] datasets will show that the proposed adaptive Retro-PHD outperforms the state-of-art particle PHD filter and the original Retro-PHD filter [9]. Other recent multiple human target trackers such as cardinality PHD filter [15] and multi-Bernoulli filter [16] are not included in this study as they do not involve backward/retrodiction processing.…”

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confidence: 99%

Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking

Feng

Wang

Naqvi

et al. 2016

IEEE Signal Process. Lett.

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The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo (SMC) implementation of the PHD filter, known as the particle PHD filter, can give state estimates with nonlinear and non-Gaussian models. Recently, Mahler et al. have introduced a PHD smoother to gain more accurate estimates for both target states and number. However, as highlighted by Psiaki in the context of a backward-smoothing extended Kalman filter, with a non-linear state evolution model the approximation error in the backward filtering requires careful consideration. Psiaki suggests to minimise the aggregated least-squares error over a batch of data. We instead use the term retrodiction PHD (Retro-PHD) filter to describe the backward filtering algorithm in recognition of the approximation error proposed in the original PHD smoother, and we propose an adaptive recursion step to improve the approximation accuracy. This step combines forward and backward processing through the measurement set and thereby mitigates the problems with the original PHD smoother when the target number changes significantly and the targets appear and disappear randomly. Simulation results show the improved performance of the proposed algorithm and its capability in handling a variable number of targets

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Governing Internet Expression: Sketching Out the Borders of a Global Default of Freedom of Expression

Wagner

2016

Law, Governance and Technology Series

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Privacy Enhancing Technologies

Cited by 9 publications

References 25 publications

Social Force Model-Based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking

Social Force Model-Based MCMC-OCSVM Particle PHD Filter for Multiple Human Tracking

Adaptive Retrodiction Particle PHD Filter for Multiple Human Tracking

Governing Internet Expression: Sketching Out the Borders of a Global Default of Freedom of Expression

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