Particle filter with occlusion handling for visual tracking

“…Many tracking algorithms have been proposed over last few years, with the aim to achieve precise tracking results. The methods include traditional feature extraction based tracking [29–33] as well as deep learning based tracking [34–36]. However, there are certain factors involve in terms of aircraft tracking, that may limit the accuracy of tracking algorithms, i.e.…”

Section: Background Studymentioning

confidence: 99%

Automatic aircraft extraction using video matting and frame registration

Amin

Riaz²,

Ghafoor

2020

IET Image Processing

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“…Particle filtering is used when the state transition and the observation models are non-linear and noise is non-Gaussian, in contrast to Kalman filters where they are linear and Gaussian, respectively [9]. Particle filters find application in a wide variety of complex problems including target tracking [15,26,32], computer vision [25], robotics [22], vehicle tracking [21,28], acoustics [2] and channel estimation in digital communication channels [29] or any application involving large, sequentially evolving data-sets [11,13].…”

Section: Introductionmentioning

confidence: 99%

Improved Particle Filter Resampling Architectures

Alam

Gustafsson

2019

J Sign Process Syst

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The most challenging aspect of particle filtering hardware implementation is the resampling step. This is because of high latency as it can be only partially executed in parallel with the other steps of particle filtering and has no inherent parallelism inside it. To reduce the latency, an improved resampling architecture is proposed which involves pre-fetching from the weight memory in parallel to the fetching of a value from a random function generator along with architectures for realizing the prefetch technique. This enables a particle filter using M particles with otherwise streaming operation to get new inputs more often than 2M cycles as the previously best approach gives. Results show that a pre-fetch buffer of five values achieves the best area-latency reduction trade-off while on average achieving an 85% reduction in latency for the resampling step leading to a sample time reduction of more than 40%. We also propose a generic division-free architecture for the resampling steps. It also removes the need of explicitly ordering the random values for efficient multinomial resampling implementation. In addition, on-the-fly computation of the cumulative sum of weights is proposed which helps reduce the word length of the particle weight memory. FPGA implementation results show that the memory size is reduced by up to 50%.

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Particle filter with occlusion handling for visual tracking

Cited by 30 publications

References 19 publications

Collaborative model tracking with robust occlusion handling

Collaborative model tracking with robust occlusion handling

Automatic aircraft extraction using video matting and frame registration

Improved Particle Filter Resampling Architectures

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