Context multi-task visual object tracking via guided filter

Wang, Yong; Luo, Xiaobing; Fu, Shan; Hu, Shiqiang

doi:10.1016/j.image.2017.12.008

Cited by 2 publications

(1 citation statement)

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“…For this, the solution was tested using comprehensive experimental analysis drawing quantitative and qualitative comparisons. Ro- bust tracking systems, such as [36], mostly involve an appearance and motion model to track the candidate states of the target. Computational complexity, however, increases proportionally with the increase in the number of targets to be tracked [37].…”

Section: Related Workmentioning

confidence: 99%

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

et al. 2020

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Human activity recognition (HAR) is an important branch of human-centered research. Advances in wearable and unobtrusive technologies offer many opportunities for HAR. While much progress has been made in HAR using wearable technology, it still remains a challenging task using unobtrusive (non-wearable) sensors. This paper investigates detection and tracking of multi-occupant HAR in a smart-home environment, using a novel low-resolution Thermal Vision Sensor (TVS). Specifically, the research presents the development and implementation of a two-step framework, consisting of a Computer Vision (CV) based method to detect and track multiple occupants combined with Convolutional Neural Network (CNN) based HAR. The proposed algorithm uses frame-difference over consecutive frames for occupant detection, a set of morphological operations to refine identified objects, and features are extracted before applying a Kalman filter for tracking. Laterally, a 19-layer CNN architecture is used for HAR and afterward the results from both methods are fused using time interval based sliding window. This approach is evaluated through a series of experiments based on benchmark Thermal Infrared datasets (VOT-TIR2016) and multi-occupant data collected from TVS. Results demonstrate that the proposed framework is capable of detecting and tracking 88.46% of multi-

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Section: Related Workmentioning

confidence: 99%

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

et al. 2020

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The Design of Intelligent Building Lighting Control System Based on CNN in Embedded Microprocessor

Ding

2023

Electronics

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A convolutional neural network (CNN) was designed and built on an embedded building lighting control system to determine whether the application of CNN could increase the accuracy of image recognition and reduce energy consumption. Currently, lighting control systems rely mainly on information technology, with sensors to detect people’s existence or absence in an environment. However, due to the deviation of this perception, the accuracy of image detection is not high. In order to validate the effectiveness of the new system based on CNN, an experiment was designed and operated. The importance of the research lies in the fact that high image detection would bring in less energy consumption. The result of the experiment indicated that, when comparing the actual position with the positioning position, the difference was between 0.01 to 0.20 m, indicating that the image recognition accuracy of the CNN-based embedded control system was very high. Moreover, comparing the luminous flux of the designed system with natural light and the designed system without natural light with the system without intelligent control, the energy savings is about 40%.

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Context multi-task visual object tracking via guided filter

Cited by 2 publications

References 32 publications

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

uMoDT: an unobtrusive multi-occupant detection and tracking using robust Kalman filter for real-time activity recognition

The Design of Intelligent Building Lighting Control System Based on CNN in Embedded Microprocessor

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