Abstract
In this paper, we investigate articulated human motion tracking from video sequences using Bayesian approach. We derive a generic particle-based filtering procedure with a low-dimensional manifold. The manifold can be treated as a regularizer that enforces a distribution over poses during tracking process to be concentrated around the low-dimensional embedding. We refer to our method as manifold regularized particle filter. We present a particular implementation of our method based on back-constrained gaussian process latent variable model and gaussian diffusion. The proposed approach is evaluated using the real-life benchmark dataset HumanEva. We show empirically that the presented sampling scheme outperforms sampling-importance resampling and annealed particle filter procedures.
Highlights
Articulated human motion tracking from video image sequences is one of the most challenging computer vision problems for the past two decades
We show empirically that the presented sampling scheme outperforms sampling-importance resampling and annealed particle filter procedures on benchmark dataset HumanEva
The best results are in bold with manifold regularized particle filter (MRPF) gave the best results except the sequence S1-Jog for which sampling importance resampling (SIR) was slightly better
Summary
Articulated human motion tracking from video image sequences is one of the most challenging computer vision problems for the past two decades. Discriminative methods are used to model directly the probability distribution over poses conditioned on the image evidence This approach is usually composed of two parts where feature extraction is followed by prediction using multivariate regression model. Pure generative modeling assumes that one tries to model the true pose space and uses Bayesian inference to combine this prior knowledge with the image evidence to estimate the current pose. Within this group of methods the two important branches have evolved. We present a novel fashion of involving information about low-dimensional embedding in the pose space into the tracking process that leads to a generic filtering procedure. In the generative approach we follow the Bayes rule to inverse the conditional probability: p(x|I) ∝ p(I|x) p(x),
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