Learning to Track On-The-Fly Using a Particle Filter with Annealed-Weighted QPSO Modeled after a Singular Dirac Delta Potential

Sengupta, Saptarshi; Peters, Richard Alan

doi:10.1109/ssci.2018.8628730

Cited by 1 publication

(3 citation statements)

References 42 publications

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“…In reference [22], the spatio-temporal context model is utilized as the filter in each convolutional neural network. In the initial frame, the target confidence map is exploited to update the spatio-temporal model.…”

Section: The Basic Principle Of Stc Algorithmmentioning

confidence: 99%

“…In the initial frame, the target confidence map is exploited to update the spatio-temporal model. The target tracking problem can be described as calculating the In reference [22], the spatio-temporal context model is utilized as the filter in each convolutional neural network. In the initial frame, the target confidence map is exploited to update the spatio-temporal model.…”

Section: The Basic Principle Of Stc Algorithmmentioning

confidence: 99%

“…Hu et al [21] developed an improved resampling cellular quantum-behaved particle swarm optimization (RScQPSO) algorithm, which was a probabilistic variant of PSO, and combined it with the PF to solve the tracking problem. Sengupta and Peters [22] constructed an evolutionary particle filter with a memory-guided proposal step size update and an improved quantum-behaved particle swarm optimization (QPSO) resampling scheme for visual tracking.…”

Section: Introductionmentioning

confidence: 99%

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Combining Spatio-Temporal Context and Kalman Filtering for Visual Tracking

Yang

Wang

et al. 2019

Mathematics

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As one of the core contents of intelligent monitoring, target tracking is the basis for video content analysis and processing. In visual tracking, due to occlusion, illumination changes, and pose and scale variation, handling such large appearance changes of the target object and the background over time remains the main challenge for robust target tracking. In this paper, we present a new robust algorithm (STC-KF) based on the spatio-temporal context and Kalman filtering. Our approach introduces a novel formulation to address the context information, which adopts the entire local information around the target, thereby preventing the remaining important context information related to the target from being lost by only using the rare key point information. The state of the object in the tracking process can be determined by the Euclidean distance of the image intensity in two consecutive frames. Then, the prediction value of the Kalman filter can be updated as the Kalman observation to the object position and marked on the next frame. The performance of the proposed STC-KF algorithm is evaluated and compared with the original STC algorithm. The experimental results using benchmark sequences imply that the proposed method outperforms the original STC algorithm under the conditions of heavy occlusion and large appearance changes.

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Section: The Basic Principle Of Stc Algorithmmentioning

confidence: 99%

Section: The Basic Principle Of Stc Algorithmmentioning

confidence: 99%