Tracking by local structural manifold learning in a new SSIR particle filter

Ding, Jianwei; Tang, Yunqi; Liu, Wei; Huang, Yongzhen; Huang, Kaiqi

doi:10.1016/j.neucom.2015.02.027

Cited by 10 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each method centres its search at the location of the target in the previous frame which may not be the most effective location, especially in the instance of a fast moving target. For this reason we employ a particle filter with a motion estimation stage which predicts the next target location based on its previous trajectory and centres the particle filter on this new location to provide a higher likelihood of locating the target [37], [38].…”

Section: Related Workmentioning

confidence: 99%

“…The sampling technique utilised in our algorithm is an SSIR particle filter [38]. This is a modification of the standard SIR particle filter in that it attempts to maintain a good distribution of particles around the probable target location even in the event of an imperfect appearance model.…”

Section: B Particle Filter With Motion Estimationmentioning

confidence: 99%

“…This is based on the theory that an image patch which contains the target will be well represented within the basis and can therefore be accurately reconstructed. Assuming that the likelihood is inversely proportional to the reconstruction error [24], [38] the image patch with the lowest error is most likely to accurately represent the target object.…”

Section: Target Likelihoodmentioning

confidence: 99%

“…Depending on the result of the tracking process, certain parameters associated with each subspace must be modified as shown in Algorithm 2. For each subspace in B 1 , the weight (ω i ) is recalculated as [38]:…”

Section: E Bags Of Subspacesmentioning

confidence: 99%

See 3 more Smart Citations

Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Jenkins

Barrie

Buggy

et al. 2018

IEEE Trans. Cybern.

View full text Add to dashboard Cite

The focus of this paper is a novel object tracking algorithm which combines an incrementally updated subspace-based appearance model, reconstruction error likelihood function and a two stage selective sampling importance resampling particle filter with motion estimation through autoregressive filtering techniques. The primary contribution of this paper is the use of multiple bags of subspaces with which we aim to tackle the issue of appearance model update. The use of a multibag approach allows our algorithm to revert to a previously successful appearance model in the event that the primary model fails. The aim of this is to eliminate tracker drift by undoing updates to the model that lead to error accumulation and to redetect targets after periods of occlusion by removing the subspace updates carried out during the period of occlusion. We compare our algorithm with several state-of-the-art methods and test on a range of challenging, publicly available image sequences. Our findings indicate a significant robustness to drift and occlusion as a result of our multibag approach and results show that our algorithm competes well with current state-of-the-art algorithms.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: B Particle Filter With Motion Estimationmentioning

confidence: 99%

Section: Target Likelihoodmentioning

confidence: 99%

Section: E Bags Of Subspacesmentioning

confidence: 99%

See 2 more Smart Citations

Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Jenkins

Barrie

Buggy

et al. 2018

IEEE Trans. Cybern.

View full text Add to dashboard Cite

show abstract

“…Many new methods have been recently proposed in the last few years. Ding et al proposed a new selective sampling importance resampling (SSIR) particle filter framework [20]. This framework integrates an auto-regressive filter to improve the process of sample generation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Grey Prediction-Based Particle Filter for Object Tracking

Yang

Lu²

2016

MCA

View full text Add to dashboard Cite

Abstract:A particle filter is a powerful tool for object tracking based on sequential Monte Carlo methods under a Bayesian estimation framework. A major challenge for a particle filter in object tracking is how to allocate particles to a high-probability density area. A particle filter does not take into account the historical prior information on the generation of the proposal distribution and, thus, it cannot approximate posterior density well. Therefore, a new fuzzy grey prediction-based particle filter (called FuzzyGP-PF) for object tracking is proposed in this paper. First, a new prediction model which was based on fuzzy mathematics theory and grey system theory was established, coined the Fuzzy-Grey-Prediction (FGP) model. Then, the history state sequence is utilized as prior information to predict and sample a part of particles for generating the proposal distribution in the particle filter. Simulations are conducted in the context of two typical maneuvering motion scenarios and the results indicate that the proposed FuzzyGP-PF algorithm can exhibit better overall performance in object tracking.

show abstract

Object tracking with particles weighted by region proposal network

Zhu

Wang

et al. 2018

Multimed Tools Appl

View full text Add to dashboard Cite

Tracking by local structural manifold learning in a new SSIR particle filter

Cited by 10 publications

References 35 publications

Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Selective Sampling Importance Resampling Particle Filter Tracking With Multibag Subspace Restoration

Fuzzy Grey Prediction-Based Particle Filter for Object Tracking

Object tracking with particles weighted by region proposal network

Contact Info

Product

Resources

About