Visible Light Dynamic Positioning Method Using Improved Camshift-Kalman Algorithm

Abstract

There are three critical elements in the visible light positioning (VLP) system: Positioning accuracy, real-time ability and robustness. However, few existing VLP studies consider these three critical elements at the same time. Especially, robustness is usually ignored in VLP system, which has a great influence on positioning performance or even leads to the failure of positioning. Therefore, we propose a novel VLP method based on image sensor (as positioning terminal), using improved Camshift-Kalman algorithm. The proposed algorithm not only combines Camshift algorithm with Kalman filter, but also introduces the Bhattacharyya coefficient innovatively. It can realize high positioning accuracy, good robustness and good real-time ability. Experiments showed that the positioning accuracy of our proposed VLP algorithm was 0.55 cm, which realized high positioning accuracy. Besides, the average processing time per frame was 27.2 ms, which realized good real-time ability. Also, whether the LED was shielded, the interference occurred or the target's velocity changed suddenly, the proposed algorithm still maintained good positioning effect, which showed that it had good robustness.

High-Accuracy and Real-Time Indoor Positioning System Based on Visible Light Communication and Mobile Robot

“…e algorithm not only combines the CAMshift algorithm with the Kalman filter but also introduces the Bhattacharyya coefficient. For more details, one could refer to our previous work [24]. We tested the effect of the algorithm in the dynamic case with modulator tubes as background interference.…”

A precise bare simulation approach to the minimization of some distances. Foundations

“…There is a vast literature on very recent applications of the Bhattacharyya coefficient, for instance it appears exemplarily in Peng & Li [289] for object tracking from successive video frames, Ayed et al [26] for efficient graph cut algorithms, Patra et al [287] for collaborative filtering in sparse data, El Merabet et al [119] for region classification in intelligent transport systems in order to compensate the lack of performance of Global Navigation Satellites Systems, Chiu et al [86] for the design of interactive mobile augmented reality systems, Noh et al [274] for dimension reduction in interacting fluid flow models, Bai et al [29] for material defect detection through ultrasonic array imaging, Dixit & Jain [115] for the design of recommender systems on highly sparse context aware datasets, Guan et al [143] for visible light positioning methods based on image sensors, Lin et al [220] for probabilistic representation of color image pixels, Chen et al [80] for distributed compressive video sensing, Jain et al [162] for the enhancement of multistage user-based collaborative filtering in recommendation systems, Pascuzzo et al [285] for brain-diffusion-MRI based early diagnosis of the sporadic Creutzfeldt-Jakob disease, Sun et al [351] for the design of automatic detection methods multitemporal (e.g. landslide) point clouds, Valpione et al [377] for the investigation of T cell dynamics in immunotherapy, Wang et al [387] for the tracking and prediction of downbursts from meteorological data, Xu et al [403] for adaptive distributed compressed video sensing for coal mine monitoring, Zhao et al [424] for the shared sparse machine learning of the affective content of images, Chen et al [82] for image segmentation and domain partitioning, De Oliveira et al [105] for the prediction of cell-penetrating peptides, Eshaghi et al [122] for the identification of multiple sclerosis subtypes through machine learning of brain MRI scans, Feng et al [125] for improvements of MRI-based detection of epilepsy-causing cortical malformations, Hanli et al [153] for designing pilot protection schemes for transmission lines, Jiang et al [170] for flow-assisted visual tracking through event cameras, Lysiak & Szmajda …”

High-Accuracy Robot Indoor Localization Scheme Based on Robot Operating System Using Visible Light Positioning

“…After receiving the raw image message using rolling shutter effect from the CMOS camera, we obtain the LED-ROI through the proposed CamKF node whose pseudo-code and design concept are shown in Code 2. The proposed CamKF node represents "LED-ROI Tracking and Extraction Method using Improved Camshift-Kalman Algorithm", which has been firstly proposed in our prior work [22]. A structure is defined in this node, whose members include Vector tracking window, LED-ID, and the x and y coordinates of the tracking window (detected LED-ROI) in the image.…”

“…More specifically, we designed a VLC localization and navigation package based on the distributed framework of the Robot Operating System (ROS), which contains the basic operation control of the robots, LED-ID detection, dynamic cm-level VLP algorithms, and so on. We have proposed LED-ID detection method based on feature recognition using Machine Learning (ML) [19]- [21], cm-level VLP algorithm just using 2 LEDs [18], anti-occlusion and anti-interference dynamic LED-ROI tracking algorithms [22], [23], in our prior demonstrations, but all of those works are limited and independently verified by simple experimental setup. For example, as for the dynamic tracking algorithm, we use the tracking error of the LED-ROI to simulate the theoretical positioning error in our previous works [22], [23].…”

“…We have proposed LED-ID detection method based on feature recognition using Machine Learning (ML) [19]- [21], cm-level VLP algorithm just using 2 LEDs [18], anti-occlusion and anti-interference dynamic LED-ROI tracking algorithms [22], [23], in our prior demonstrations, but all of those works are limited and independently verified by simple experimental setup. For example, as for the dynamic tracking algorithm, we use the tracking error of the LED-ROI to simulate the theoretical positioning error in our previous works [22], [23]. Although the tracking error can reflect the performance of the algorithm, it is still no experimental verification with the positioning algorithm.…”