Integrating low-resolution surveillance camera and smartphone inertial sensors for indoor positioning

Zhang, Jiuxin; Zhou, Pingqiang

doi:10.1109/plans.2018.8373408

Cited by 3 publications

(15 citation statements)

References 21 publications

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“…Other than AVINS, the sensors in this system are distributed on different platforms and further data transformation is needed before sensing integration. Some of the recent studies utilize this idea to provide 2D locations [22][23][24]. These studies integrate the visual results from a single surveillance camera and PDR results from the embedded IMU sensors in the smartphone to continuously track 2D user movements in either indoor or outdoor environments.…”

Section: Related Workmentioning

confidence: 99%

“…Another study conducted by Shanghai Technology University [24] combines the two systems by matching the gait features from both visual and PDR system. For visual tracking, their system also installs the camera to view the whole scene.…”

Section: Related Workmentioning

confidence: 99%

“…Second, this study provides a simple but effective novel algorithm to integrate PDR and visual tracking systems. Its performance has achieved at least 20% accuracy improvement of synthesized results in an environment with over 65% entirely invisible areas, when the previous 2D PVINSs are applied in environments with less than 50% partial occlusions [22][23][24]. Third, it contributes a novel algorithm to detect different floors and even their transition areas as the 3D information by using a smartphone-embedded barometer.…”

Section: Introductionmentioning

confidence: 95%

“…PDR systems can be categorized into several groups with respect to where they are deployed and so what constraints can be applied. They include the foot-mounted [15][16][17][18][19][20], waistmounted [21] and hand-held systems [22][23][24][25][26]. This study proposes and implements a novel PDR system for handheld smartphones, to make most of their wide use and ubiquity [27,28], and also the miniaturized and low-cost sensors that are embedded in the phone [1].…”

Section: Introductionmentioning

confidence: 99%

“…Some recent studies [11,[22][23][24] have proved that the integration of these two sensing systems, also known as Visionaided Inertial System (VINS), can keep the advantages of both positioning systems while providing 2D localization service with higher accuracy, continuity, accessibility, and reliability [13,33]. However, the previous passive VINSs (PVINSs) only focus on providing the 2D positions in a fixed scene with a single camera using self-trained pedestrian detectors [11,[22][23][24]. To overcome these limitations, this study contributes the first multi-camera 3D PVINS, with the ability of handling multi-scene shifting.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

Yan

Basiri

et al. 2020

IEEE Trans. Instrum. Meas.

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The vision-aided Pedestrian Dead Reckoning (PDR) systems have become increasingly popular, thanks to the ubiquitous mobile phone embedded with several sensors. This is particularly important for indoor use, where other indoor positioning technologies require additional installation or bodyattachment of specific sensors. This paper proposes and develops a novel 3D Passive Vision-aided PDR system that uses multiple surveillance cameras and smartphone-based PDR. The proposed system can continuously track users' movement on different floors by integrating results of inertial navigation and Faster R-CNNbased real-time pedestrian detection, while utilizing existing camera locations and embedded barometers to provide floor/height information to identify user positions in 3D space. This novel system provides a relatively low-cost and user-friendly solution, which requires no modifications to currently available mobile devices and also the existing indoor infrastructures available at many public buildings for the purpose of 3D indoor positioning. This paper shows the case of testing the prototype in a four-floor building, where it can provide the horizontal accuracy of 0.16m and the vertical accuracy of 0.5m. This level of accuracy is even better than required accuracy targeted by several emergency services, including the Federal Communications Commission (FCC). This system is developed for both Android and iOS-running devices.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

Yan

Basiri

et al. 2020

IEEE Trans. Instrum. Meas.

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Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model

Luo

et al. 2019

IEEE Access

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In recent years, multi-object tracking has attracted more and more attention, both in academia and engineering, but most of the recent works do not pay attention to the speed of the algorithm and only pursue the accuracy. In this paper, we propose an online multi-pedestrian tracking algorithm, taking into account both the accuracy and the speed. First, the motion models of the targets are established by the Kalman filter. At the same time, the appearance models of the targets are extracted by the convolutional neural network. Moreover, a data association algorithm is proposed, which integrates the motion information, including scale, intersection-over-union, and distance, and the appearance information, including the current appearance model and the long-term appearance model. With the data association algorithm, the matching between detections and tracklets is realized, and the goal of tracking by detection is achieved. We compare the proposed algorithm with other algorithms on the MOT15 benchmark and the MOT16 benchmark. The experiment results show that the algorithm has high accuracy and good real-time performance. INDEX TERMS Online, pedestrian detection, multi-object tracking, re-identifying, Kalman filter, data association.

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Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Sharif

2021

IEEE Access

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Privacy of people is a key factor in surveillance systems. Video camera brings us welloff color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.

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Integrating low-resolution surveillance camera and smartphone inertial sensors for indoor positioning

Cited by 3 publications

References 21 publications

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

3-D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning

Fast Online Multi-Pedestrian Tracking via Integrating Motion Model and Deep Appearance Model

Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

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