Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room

Jia, Li; Radke, Richard J.

doi:10.1109/tii.2013.2251892

Cited by 52 publications

(34 citation statements)

References 17 publications

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“…By detecting the occupant enter which geofenced sub-area, the direction of air flow is determined. Compared to infrared radiation based PIR sensor or proximity detection based RFID, camera-based vision analysis technique (Benezeth et al 2011, Jia & Radke 2014, Gaspar & Oliveira 2015 for indoor tracking is able to provide highly accurate position estimates with the resolution being from 0.01 cm to 1 cm (Mautz 2012). The approach brings convenience for the occupants since there is no need for them to carry extra devices.…”

Section: Occupancy Information For Coolingmentioning

confidence: 99%

A tracking cooling fan using geofence and camera-based indoor localization

Liu

Yin

et al. 2017

Building and Environment

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Highlights Human positions are tracked accurately by a camera-based indoor tracking system  Air flow is directed to the desired direction by using virtual geofences  Desired air speed is provided based on the PMV-SET thermal comfort model  Fan speed setting is adjusted by a calibrated mapping algorithm based on the estimation of occupant-fan distance  The proposed system can be used for thermal comfort improvement and energy conservation AbstractCompressor-based cooling systems have a large impact on energy consumption in modern buildings, particularly in the tropics. Elevated air movement by electric fans is a cost-effective cooling method for both energy saving and thermal comfort improvement. In this paper, a tracking cooling fan using geofence and camera-based indoor localization is proposed. Personal cooling service is provided based on the detection of the occupant in the area bounded by virtual geofences. The proposed camera-based indoor tracking system is able to accurately locate the positions of the occupant, determine the direction of air flow, and calculate the occupant-fan distance. The tracking fan is able to provide the needed air speed determined by PMV-SET thermal comfort model using a calibrated mapping algorithm. The effectiveness of the proposed system has been verified through experiments which show that the system is able to operate with low power while improving thermal comfort. The system can be used in an air-conditioned environment with higher temperature setpoint to save energy or in a naturally conditioned environment for thermal comfort enhancement.

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Section: Occupancy Information For Coolingmentioning

confidence: 99%

A tracking cooling fan using geofence and camera-based indoor localization

Liu

Yin

et al. 2017

Building and Environment

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“…and correctly identify the object even when the object is occluded with moving subjects (such as moving hands or books). Object detection and tracking system without occlusion handling using depth-only image was developed in [13], while detection and tracking of multiple objects with occlusion detection but no identification during the occlusion were presented in [14] and [15]. Our proposed depth-based object detection and tracking method enables individual object identification for each object during occlusion.…”

Section: Related Workmentioning

confidence: 99%

Two-layer tracking for occlusion handling and inter-sensor identification in multiple depth sensorsbased object detection and tracking

Sabirin

Naito

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The main challenge in depth-based object detection and tracking process is to provide correct identification of the detected objects during occlusion. This is because the information necessary to distinguish and consequently identify the objects throughout the occlusion events are limited, compared to conventional, color-based object tracking. In this paper we propose a two-layer tracking method that enables automatic occlusion handling and inter-sensor identification for object detection and tracking that utilizes more than one depth sensor. On the first layer, the tracking is first performed independently for each sensor to extract objects' feature and perform initial tracking with separation of the occluded objects. On the second layer, the tracking is performed in the perspective projection of the objects tracked on the first layer that are combined in a single processing plane to provide correct identification of the objects that are detected in one sensor to another. Experiment results show that the proposed method can correctly identified occluded objects and objects that are moving between sensors coverage area.

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“…To track these moving clusters, an EM algorithm is used to estimate the parameters of those clusters. Multiple range sensors are used by Jia and Radke [17] to produce depth information of objects in a room. Object tracking is performed upon the depth information of the moving objects by observing the weighted centroid of each detected object.…”

Section: Related Workmentioning

confidence: 99%

Utilizing Attributed Graph Representation in Object Detection and Tracking for Indoor Range Sensor Surveillance Cameras

Sabirin

Suzuki

Naito

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThe problem of identifying moving objects in a video recording produced by a range sensor camera is due to the limited information available for classifying different objects. On the other hand, the infrared signal from a range sensor camera is more robust for extreme luminance intensity when the monitored area has light conditions that are too bright or too dark. This paper proposes a method of detection and tracking moving objects in image sequences captured by stationary range sensor cameras. Here, the depth information is utilized to correctly identify each of detected objects. Firstly, camera calibration and background subtraction are performed to separate the background from the moving objects. Next, a 2D projection mapping is performed to obtain the location and contour of the objects in the 2D plane. Based on this information, graph matching is performed based on features extracted from the 2D data, namely object position, size and the behavior of the objects. By observing the changes in the number of objects and the objects' position relative to each other, similarity matching is performed to track the objects in the temporal domain. Experimental results show that by using similarity matching, object identification can be correctly achieved even during occlusion.

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Using Time-of-Flight Measurements for Privacy-Preserving Tracking in a Smart Room

Cited by 52 publications

References 17 publications

A tracking cooling fan using geofence and camera-based indoor localization

A tracking cooling fan using geofence and camera-based indoor localization

Two-layer tracking for occlusion handling and inter-sensor identification in multiple depth sensorsbased object detection and tracking

Utilizing Attributed Graph Representation in Object Detection and Tracking for Indoor Range Sensor Surveillance Cameras

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