Labelled Non-zero Particle Flow for SMC-PHD Filtering

Liu, Yang; Hu, Qinghua; Zou, Yuexian; Wang, Wenwu

doi:10.1109/icassp.2019.8683399

Cited by 38 publications

(54 citation statements)

References 21 publications

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“…The PHD filter is a computationally cheaper alternative to the RFS which is the first-order approximation of the RFS and propagates only the first order moments instead of the full multi-target posterior [20], [24], [25], [26], [27], [28], [29], [30]. The PHD filter propagates the intensity function of the multi-target posterior.…”

Section: Phd Filtermentioning

confidence: 99%

Quick Overview for Multi-target Tracking

Ben¹

2020

Preprint

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Tracking an unknown and time-varying number of targets (e.g., speakers) in indoor environments using audio-visual (AV) modalities has received increasing interest in numerous fields including video conferencing, individual speaker discrimination, and human-computer interaction. The audio-visual sequential Monte Carlo probability hypothesis density (AV-SMC-PHD) filter is a popular baseline for multi-target tracking, offering an elegant framework for fusing audio-visual information and dealing with a varying number of speakers. However, the performance of this filter can be adversely affected by the weight degeneracy problem, where the weights of most of the particles may become very small, while only few remain significant, during the iteration of the algorithm. In this paper, we will short discuss the multi-target tracking.

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Section: Phd Filtermentioning

confidence: 99%

Quick Overview for Multi-target Tracking

Ben¹

2020

Preprint

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“…We are using Kalman filter [5]- [7], [15] to track the marker smoothly and getting rid of unwanted jerks. The Kalman filter is a framework for estimating a process's state, and using measurements to correct or update these estimations.…”

Section: H Tracking Techniquementioning

confidence: 99%

Hand Gesture Recognition With a Novel Particle Filter

Huerta¹,

Crawford²,

Brown³

2020

Preprint

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Human Computer Interaction (HCI) has been redefined in this era. People want to interact with their devices in such a way that has physical significance in the real world, in other words, they want ergonomic input devices. In this paper, we propose a new method of interaction with computing devices having a consumer grade camera, that uses two colored markers (red and green) worn on tips of the fingers to generate desired hand gestures, and for marker detection and tracking we usedtemplate matching with kalman filter. We have implemented all the usual system commands, i.e., cursor movement, right click, left click, double click, going forward and backward, zoom in and out through different hand gestures. Our system caneasily recognize these gestures and give corresponding system commands. Our system is suitable for both desktop devices and devices where touch screen is not feasible like large screens or projected screens.

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“…Multimodal perception is fertile research ground that merits further investigation, and has been extensively used in cognitive science, behavioral science, and neuroscience owing to its capabilities of enabling brains to learn meaningful information from different sensory modalities, including sound, sight etc [1]. Audio and vision, as the major perception peripheral in Human Computer Interaction (HCI) systems, convey significant and complementary information for scene understanding [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Speaker Localization: Audio-Refined Visual Scaling Factor Estimation

Qian

Liu

Wang

et al. 2021

IEEE Signal Process. Lett.

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Neither a monocular RGB camera nor a smallsize microphone array is capable of accurate three-dimensional (3D) speaker localization. By taking advantage of accurate visual object detection, and audio-visual complementary sensor fusion, we formulate the three-dimensional (3D) speaker localization problem as a visual scaling factor estimation problem. As a result, we effectively reduce the traditional audio-only 3D speaker localization from an exhaustive grid search to a one-dimensional (1D) optimization problem. We propose a multi-modal perception system with two optimization approaches. We show that the proposed methods are effective, accurate, and robust against interference and, as corroborated by indicative empirical results on real dataset, competitive to the conventional uni-modal and the state-of-the-art audio-visual speaker localization approaches.

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Labelled Non-zero Particle Flow for SMC-PHD Filtering

Cited by 38 publications

References 21 publications

Quick Overview for Multi-target Tracking

Quick Overview for Multi-target Tracking

Hand Gesture Recognition With a Novel Particle Filter

Three-Dimensional Speaker Localization: Audio-Refined Visual Scaling Factor Estimation

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