Pedestrian Tracking Using Particle Filter Algorithm

Xu, Fen; Gao, Ming

doi:10.1109/icece.2010.364

Cited by 7 publications

(2 citation statements)

References 7 publications

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“…This leads to better performance in pedestrian tracking, as the Jacobian computation is not necessary anymore, with no or minimum increase of the computational cost [15]. d) Particle Filter: This is a sample-based estimator widely used for pedestrian tracking, based on Monte Carlo methods [80], [145], [231]. Unlike EKF, which deals with Gaussian and linearized distributions, it performs state estimation of non-linear and non-Gaussian distributions.…”

Section: A Single Pedestrian Point Trackingmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motions. Planning AV actions in the presence of pedestrians thus requires modelling of their probable future behaviour as well as detecting and tracking them. This narrative review article is Part I of a pair, together surveying the current technology stack involved in this process, organising recent research into a hierarchical taxonomy ranging from low-level image detection to high-level psychology models, from the perspective of an AV designer. This self-contained Part I covers the lower levels of this stack, from sensing, through detection and recognition, up to tracking of pedestrians. Technologies at these levels are found to be mature and available as foundations for use in high-level systems, such as behaviour modelling, prediction and interaction control.

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Section: A Single Pedestrian Point Trackingmentioning

confidence: 99%

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Camara

Bellotto

Coşar

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…The algorithms which utilize the deterministic method are background subtraction ((McIvor, 2000); (LIU et al, 2001)), inter-frame difference ((Lipton et al, 1998); (Collins et al, 2000)), optical flow (Meyer et al, 1998), skin color extraction ((kyung-min Cho et al, 2001); (Phung et al, 2003)) and so on. On the other hand, the stochastic methods use the state space to model the underlying dynamics of the tracking system such as Kalman filter (Broida and Chellappa, 1986) and particle filter ((Isard and Blake, 1998); (Ristic et al, 2004); (Sugandi et al, 2009); (Fen and Ming., 2010); (Zhiqiang et al, 2011); (Zhonga et al, 2012) ).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Iterated Kalman Particle Filter for Object Tracking Problems

Nagy

Ali

Zayed

2013

Proceedings of the International Conference on Computer Vision Theory and Applications

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Particle Filters (PFs), are widely used where the system is non Linear and non Gaussian. Choosing the importance proposal distribution is a key issue for solving nonlinear filtering problems. Practical object tracking problems encourage researchers to design better candidate for proposal distribution in order to gain better performance. In this correspondence, a new algorithm referred to as the hybrid iterated Kalman particle filter (HIKPF) is proposed. The proposed algorithm is developed from unscented Kalman filter (UKF) and iterated extended Kalman filter (IEKF) to generate the proposal distribution, which lead to an efficient use of the latest observations and generates more close approximation of the posterior probability density. Comparing with previously suggested methods(e.g PF, PF-EKF, PF-UKF, PF-IEKF), our proposed method shows a better performance and tracking accuracy. The correctness as well as validity of the algorithm is demonstrated through numerical simulation and experiment results.

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Particle Filter Based on Multiple Cues Fusion for Pedestrian Tracking

Chong

Chen

et al. 2012

Communications in Computer and Information Science

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Pedestrian Tracking Using Particle Filter Algorithm

Cited by 7 publications

References 7 publications

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Pedestrian Models for Autonomous Driving Part I: Low-Level Models, From Sensing to Tracking

Hybrid Iterated Kalman Particle Filter for Object Tracking Problems

Particle Filter Based on Multiple Cues Fusion for Pedestrian Tracking

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