Towards privacy-preserving activity recognition using extremely low temporal and spatial resolution cameras

Dai, Ji; Wu, Q. M. Jonathan; Saghafi, Behrouz; Konrad, Janusz; Ishwar, Prakash

doi:10.1109/cvprw.2015.7301356

Cited by 48 publications

(35 citation statements)

References 13 publications

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“…Whether at home or at work, a smart room promises to bring improved energy efficiency, health benefits, and productivity [5]. Energy savings can be realized, for example, by lowering illumination and heating/cooling in regions void of humans, while health benefits can be obtained by optimizing lighting conditions for individual activities, e.g., reducing screen glare when working on a laptop.…”

Section: Introductionmentioning

confidence: 98%

Estimating head pose orientation using extremely low resolution images

Chen

Richter

et al. 2016

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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Abstraci-The estimation of human head pose is of interest in some surveillance and human-computer interaction scenarios.Tr aditionally, this is not a difficult task if high-or even standard definition video cameras are used. However, such cameras cannot be used in scenarios requiring privacy protection. In this paper, we propose a non-linear regression method for the estimation of human head pose from extremely low resolution images captured by a monocular RGB camera. We evaluate the common histogram of oriented gradients (HoG) feature, propose a new gradient-based feature, and use Support Vector Regression (SVR) to estimate head pose. We evaluate our algorithm on the Biwi Kinect Head Pose Dataset by re-sizing full-resolution RGB images to extremely low resolutions. The results are promising. At 10 x lO-pixel resolution, we achieve 6.95, 9.92 and 12.88 degree mean-absolute errors (MAE) for roll, yaw and pitch angles, respectively. These errors are very close to state-of-the-art results for full-resolution images.

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Section: Introductionmentioning

confidence: 98%

Estimating head pose orientation using extremely low resolution images

Chen

Richter

et al. 2016

2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)

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“…Privacy preserving occupancy sensing for smart lighting applications was recently addressed by Wang et al [18], who used modulated light and distributed color sensors to estimate the 3D occupancy of an indoor space. Dai et al [4] presented a method for activity recognition in a smart room using extremely low resolution cameras. Most relevant to this paper, Jia and Radke [10] introduced the idea of downward-pointed ToF rays for occupancy and pose estimation.…”

Section: Related Workmentioning

confidence: 99%

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Bhattacharya

Radke

2016

2016 IEEE Winter Conference on Applications of Computer Vision (WACV)

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We present a method for real-time person tracking and coarse pose estimation in a smart room using a sparse array of single pixel time-of flight (ToF) sensors mounted in the ceiling of the room. The single pixel sensors are relatively inexpensive compared to commercial ToF cameras and are privacy preserving in that they only return the range to a small set of hit points. The tracking algorithm includes higher level logic about how people move and interact in a room and makes estimates about the locations of people even in the absence of direct measurements. A maximum likelihood classifier based on features extracted from the time series of ToF measurements is used for robust pose classification into sitting, standing and walking states. We use both computer simulation and real-world experiments to show that the algorithms are capable of robust person tracking and pose estimation even with a sensor spacing of 60 cm (i.e., 1 sensor per ceiling tile).

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“…However, there are challenging issues in vision-based methods, such as computational complexity in image processing, data consistency under different illumination conditions, and privacy infringement of the human target [8]. These problems make the practical deployment of vision-based systems difficult.…”

Section: Introductionmentioning

confidence: 99%

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

Luo

Tan

Guan

et al. 2016

Sensors

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Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.

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Towards privacy-preserving activity recognition using extremely low temporal and spatial resolution cameras

Cited by 48 publications

References 13 publications

Estimating head pose orientation using extremely low resolution images

Estimating head pose orientation using extremely low resolution images

Arrays of single pixel time-of-flight sensors for privacy preserving tracking and coarse pose estimation

Abnormal Activity Detection Using Pyroelectric Infrared Sensors

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