Particle filter to track multiple people for visual surveillance

Sherrah, Jamie; Ristić, Branko; Redding, N.J.

doi:10.1049/iet-cvi.2010.0026

Cited by 19 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To verify the stability and robustness of particles sampling under the foreground restraint, and the effectiveness of tracking under complex background with neighbor-information CodeBook feature extraction, we make experiments with algorithm of literature [6] and improved particle filter with two videos. Coped with the combination of neighbor-information CodeBook, the improved algorithm reduces the searching range of particles, so particles only to search around the target, which has a precise and stable tracking result.…”

Section: The Experimental Results and Analysismentioning

confidence: 99%

Research on Moving Target Tracking and Information Filtering in Complex Building Environment

Bu¹

2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

View full text Add to dashboard Cite

An algorithm of target tracking based on information filteringBook and particle filter is proposed in this paper. Accurate foreground region through the fusion of neighbour-information is segmented and features of foreground are extract to build an initial template for particle filter. The experimental results show that this algorithm can overcome the interference of target intersection and complex background environment to track the target stably and better than the particle filter algorithm in literature on the accuracy, robustness and real-time performance.

show abstract

Section: The Experimental Results and Analysismentioning

confidence: 99%

Research on Moving Target Tracking and Information Filtering in Complex Building Environment

Bu¹

2016

Proceedings of the 2016 3rd International Conference on Materials Engineering, Manufacturing Technology and Control

View full text Add to dashboard Cite

show abstract

“…But its object representation is not suitable which is a centroid and the complexity of the technique also increases as accuracy is improved. Sherrah, Ristic, and Redding (2011) discuss the adaptation of the particle filter to track people in surveillance videos. Walia and Kapoor (2014) embed the improved cuckoo search algorithm into the particle filter framework.…”

Section: Object Trackingmentioning

confidence: 99%

Modified foreground segmentation for object tracking using wavelets in a tensor framework

Kapoor

Rohilla

2014

International Journal of Electronics

View full text Add to dashboard Cite

Subspace-based techniques have become important in behaviour analysis, appearance modelling and tracking. Various vector and tensor subspace learning techniques are already known that perform their operations in offline as well as in an online manner. In this work, we have improved upon a tensor-based subspace learning by using fourth-order decomposition and wavelets so as to have an advanced adaptive algorithm for robust and efficient background modelling and tracking in coloured video sequences. The proposed algorithm known as fourth-order incremental tensor subspace learning algorithm uses the spatio-colour-temporal information by adaptive online update of the means and the eigen basis for each unfolding matrix using tensor decomposition to fourth-order image tensors. The proposed method employs the wavelet transformation to an optimum decomposition level in order to reduce the computational complexity by working on the approximate counterpart of the original scenes and also reduces noise in the given scene. Our tracking method is an unscented particle filter that utilises appearance knowledge and estimates the new state of the intended object. Various experiments have been performed to demonstrate the promising and convincing nature of the proposed method and the method works better than existing methods.

show abstract

“…In the visual tracking problem, the movement of the object is unable to predict, the object state in the current frame only related to that in the prior frame; then the visual tracking process satisfies the Markov process [23]. A bounding box = ( , , , ℎ ) is used to describe the object state at the th frame, where ( , ), , ℎ denote the upper left corner coordinate, the width, and height of the bounding box.…”

Section: Sequence Inferencementioning

confidence: 99%

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

Deng

Dai

Dong

et al. 2014

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In the visual tracking scenarios, if there are multiple objects, due to the interference of similar objects, tracking may fail in the progress of occlusion to separation. To address this problem, this paper proposed a visual tracking algorithm with discrimination through multimanifold learning. Color-gradient-based feature tensor was used to describe object appearance for accommodation of partial occlusion. A prior multimanifold tensor dataset is established through the template matching tracking algorithm. For the purpose of discrimination, tensor distance was defined to determine the intramanifold and intermanifold neighborhood relationship in multimanifold space. Then multimanifold discriminate analysis was employed to construct multilinear projection matrices of submanifolds. Finally, object states were obtained by combining with sequence inference. Meanwhile, the multimanifold dataset and manifold learning embedded projection should be updated online. Experiments were conducted on two real visual surveillance sequences to evaluate the proposed algorithm with three state-of-the-art tracking methods qualitatively and quantitatively. Experimental results show that the proposed algorithm can achieve effective and robust effect in multi-similar-object mutual occlusion scenarios.

show abstract

Particle filter to track multiple people for visual surveillance

Cited by 19 publications

References 13 publications

Research on Moving Target Tracking and Information Filtering in Complex Building Environment

Research on Moving Target Tracking and Information Filtering in Complex Building Environment

Modified foreground segmentation for object tracking using wavelets in a tensor framework

Visual Tracking via Feature Tensor Multimanifold Discriminate Analysis

Contact Info

Product

Resources

About