Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
نویسنده
چکیده
We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will then be used to classify object trajectories, predict the next motion state, and provide Gaussian process dynamical samples for the particle filter. In addition, Histogram-Bhattacharyya, GMM Kullback-Leibler, and the rotation invariant appearance models are employed, respectively, and compared in the particle filter as complimentary features to coordinate data used in GPDM. The simulation results demonstrate that the approach can track more than four targets with reasonable runtime overhead and performance. In addition, it can successfully deal with occasional missing frames and temporary occlusion.
منابع مشابه
Multiple Human Tracking Using Particle Filter with Gaussian Process Dynamical Model
We present a particle filter-based multitarget tracking method incorporating Gaussian process dynamical model (GPDM) to improve robustness in multitarget tracking. With the particle filter Gaussian process dynamical model (PFGPDM), a highdimensional target trajectory dataset of the observation space is projected to a low-dimensional latent space in a nonlinear probabilistic manner, which will t...
متن کامل3d Human Tracking with Gaussian Process Annealed
We present an approach for tracking human body parts with prelearned motion models in 3D using multiple cameras. We use an annealed particle filter to track the body parts and a Gaussian Process Dynamical Model in order to reduce the dimensionality of the problem, increase the tracker's stability and learn the motion models. We also present an improvement for the weighting function that helps t...
متن کاملConvolutional Gating Network for Object Tracking
Object tracking through multiple cameras is a popular research topic in security and surveillance systems especially when human objects are the target. However, occlusion is one of the challenging problems for the tracking process. This paper proposes a multiple-camera-based cooperative tracking method to overcome the occlusion problem. The paper presents a new model for combining convolutiona...
متن کاملA New Modified Particle Filter With Application in Target Tracking
The particle filter (PF) is a novel technique that has sufficiently good estimation results for the nonlinear/non-Gaussian systems. However, PF is inconsistent that caused mainly by loss of particle diversity in resampling step and unknown a priori knowledge of the noise statistics. This paper introduces a new modified particle filter called adaptive unscented particle filter (AUPF) to overcome th...
متن کاملUsing Gaussian Process Annealing Particle Filter for 3D Human Tracking
We present an approach for human body parts tracking in 3D with prelearned motion models using multiple cameras. Gaussian process annealing particle filter is proposed for tracking in order to reduce the dimensionality of the problem and to increase the tracker’s stability and robustness. Comparing with a regular annealed particle filter-based tracker, we show that our algorithm can track bette...
متن کامل