Privacy-preserving human action recognition as a remote cloud service using RGB-D sensors and deep CNN

Rajput, Amitesh Singh; Raman, Balasubramanian; Imran, Javed

doi:10.1016/j.eswa.2020.113349

Cited by 21 publications

(19 citation statements)

References 20 publications

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“…Their method involves feature extraction and representation and uses RGBD cameras. Rajput et al [6] also employed RGBD cameras and deep CNN in order to achieve privacy preserving human action recognition. They reused cloud based learned models for better performance.…”

Section: Privacy Aware Action Recognitionmentioning

confidence: 99%

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A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

Kumar¹,

Jonnadula²

2021

Bulletin EEI

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There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.

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Section: Privacy Aware Action Recognitionmentioning

confidence: 99%

“…It could force similarity between input image and modified image (some visual similarity). This loss function is expressed as in (6).…”

Section: Figure 3 Overview Of the Proposed Framework Based On Adversarial Learningmentioning

confidence: 99%

A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

Kumar¹,

Jonnadula²

2021

Bulletin EEI

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“…Such advances are relevant to the field of assisted living. To address privacy concerns, a secure cloud-based solution [ 12 ] is proposed for recognizing human actions using color and depth data. For this solution, researchers only collected motion history images [ 13 ] generated from color data and depth motion maps generated from depth data, using a deep convolutional neural network (CNN) to perform recognition.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera

Zin

Htet

Akagi

et al. 2021

Sensors

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Smart technologies are necessary for ambient assisted living (AAL) to help family members, caregivers, and health-care professionals in providing care for elderly people independently. Among these technologies, the current work is proposed as a computer vision-based solution that can monitor the elderly by recognizing actions using a stereo depth camera. In this work, we introduce a system that fuses together feature extraction methods from previous works in a novel combination of action recognition. Using depth frame sequences provided by the depth camera, the system localizes people by extracting different regions of interest (ROI) from UV-disparity maps. As for feature vectors, the spatial-temporal features of two action representation maps (depth motion appearance (DMA) and depth motion history (DMH) with a histogram of oriented gradients (HOG) descriptor) are used in combination with the distance-based features, and fused together with the automatic rounding method for action recognition of continuous long frame sequences. The experimental results are tested using random frame sequences from a dataset that was collected at an elder care center, demonstrating that the proposed system can detect various actions in real-time with reasonable recognition rates, regardless of the length of the image sequences.

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“…H Uman Action Recognition (HAR) in videos has gained immense attention than preceding because of occupying added importance and usefulness in diverse applications [1] [71]. From the last decades, visualbased deep learning methods have created a new era offering satisfying results on classification, recognition and detection tasks for the still images [11] [12] [13].…”

Section: Introductionmentioning

confidence: 99%

“…Numerous, action recognition techniques based on depth information along with RGB frames using RGB-D dataset for 3D action recognition [65] [71], attention mechanism [33] - [40] and skeleton modality [14] [70] [72] have been introduced to deal with the addressed problem. These published methods demand to pre-process the data, which increases the latency and time complexity during prediction.…”

Section: Introductionmentioning

confidence: 99%

Batch Entropy Supervised Convolutional Neural Networks for Feature Extraction and Harmonizing for Action Recognition

et al. 2020

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Privacy-preserving human action recognition as a remote cloud service using RGB-D sensors and deep CNN

Cited by 21 publications

References 20 publications

A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

A multi-task learning based hybrid prediction algorithm for privacy preserving human activity recognition framework

Real-Time Action Recognition System for Elderly People Using Stereo Depth Camera

Batch Entropy Supervised Convolutional Neural Networks for Feature Extraction and Harmonizing for Action Recognition

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