Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming

Zhou, Jizhe; Pun, Chi‐Man

doi:10.1109/tifs.2020.3029913

Cited by 31 publications

(12 citation statements)

References 51 publications

(87 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Utilising the aforementioned object-detection AI algorithms, identification of people and, by extension, faces would allow for this context to be concatenated to the signal and mobility-based gait characteristics, providing the context that the participant was talking while walking. Furthermore, by identifying an individual in that context, the same AI could blur the face of the individual to protect their identity [ 50 , 51 ], upholding ethical concerns.…”

Section: Resultsmentioning

confidence: 99%

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Moore

Stuart

McMeekin

et al. 2023

Sensors

View full text Add to dashboard Cite

Fall risk assessment needs contemporary approaches based on habitual data. Currently, inertial measurement unit (IMU)-based wearables are used to inform free-living spatio-temporal gait characteristics to inform mobility assessment. Typically, a fluctuation of those characteristics will infer an increased fall risk. However, current approaches with IMUs alone remain limited, as there are no contextual data to comprehensively determine if underlying mechanistic (intrinsic) or environmental (extrinsic) factors impact mobility and, therefore, fall risk. Here, a case study is used to explore and discuss how contemporary video-based wearables could be used to supplement arising mobility-based IMU gait data to better inform habitual fall risk assessment. A single stroke survivor was recruited, and he conducted a series of mobility tasks in a lab and beyond while wearing video-based glasses and a single IMU. The latter generated topical gait characteristics that were discussed according to current research practices. Although current IMU-based approaches are beginning to provide habitual data, they remain limited. Given the plethora of extrinsic factors that may influence mobility-based gait, there is a need to corroborate IMUs with video data to comprehensively inform fall risk assessment. Use of artificial intelligence (AI)-based computer vision approaches could drastically aid the processing of video data in a timely and ethical manner. Many off-the-shelf AI tools exist to aid this current need and provide a means to automate contextual analysis to better inform mobility from IMU gait data for an individualized and contemporary approach to habitual fall risk assessment.

show abstract

Section: Resultsmentioning

confidence: 99%

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Moore

Stuart

McMeekin

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Prior research indicated that privacy was a core value in system design [29] and people with visual impairments were concerned about their privacy when using technologies [11,21,34]. To protect users' privacy, Zhou et al developed a technology to blur irrelevant people's faces during live-streaming [79]. However, many questions remain unresolved.…”

Section: Design For Inclusive Computer Vision Technologymentioning

confidence: 99%

"It Feels Like Being Locked in A Cage": Understanding Blind or Low Vision Streamers' Perceptions of Content Curation Algorithms

Rong¹,

Zhou²,

Lu³

et al. 2022

Preprint

View full text Add to dashboard Cite

Blind or low vision (BLV) people were recently reported to be live streamers on the online platforms that employed content curation algorithms. Recent research uncovered algorithm biases suppressing the content created by marginalized populations. However, little is known about the effects of the algorithms adopted by live streaming platforms on BLV streamers and how they, as a marginalized population, perceive the effects of the algorithms. We interviewed BLV streamers (N=19) of Douyin -a popular live stream platform in China -to understand their perceptions of algorithms, perceived challenges, and mitigation strategies. Our findings show the perceived factors contributing to disadvantages under algorithmic evaluation of BLV streamers' content (e.g., issues with filming and timely interaction with viewers) and perceived algorithmic suppression (e.g., content not amplified to sighted users but suppressed within the BLV community). Their mitigation strategies (e.g., not watching other BLV streamers' shows) tended to be passive. We discuss design considerations to design a more inclusive and fair live streaming platform.CCS Concepts: • Human-centered computing → Human computer interaction (HCI); Empirical studies in HCI .

show abstract

“…The design of the HDMI's driver reads the frame data from the BRAM through the copy provided by the user() function. Based on this, the data frames can be processed by OpenCV program for face detection and tracking [29].…”

Section: Linux Device Driver Programmingmentioning

confidence: 99%

Intelligent Security Monitoring System Based on RISC-V SoC

Yuan

et al. 2021

Electronics

View full text Add to dashboard Cite

With the development of the economy and society, the demand for social security and stability increases. However, traditional security systems rely too much on human resources and are affected by uncontrollable community security factors. An intelligent security monitoring system can overcome the limitations of traditional systems and save human resources, contributing to public security. To build this system, a RISC-V SoC is first designed in this paper and implemented on the Nexys-Video Artix-7 FPGA. Then, the Linux operating system is transplanted and successfully run. Meanwhile, the driver of related hardware devices is designed independently. After that, three OpenCV-based object detection models including YOLO (You Only Look Once), Haar (Haar-like features), and LBP (Local Binary Pattern) are compared, and the LBP model is chosen to design applications. Finally, the processing speed of 1.25 s per frame is realized to detect and track moving objects. To sum up, we build an intelligent security monitoring system with real-time detection, tracking, and identification functions through hardware and software collaborative design. This paper also proposes a video downsampling technique. Based on this technique, the BRAM resource usage on the hardware side is reduced by 50% and the amount of pixel data that needs to be processed on the software side is reduced by 75%. A video downsampling technology is also proposed in this paper to achieve better video display effects under limited hardware resources. It provides conditions for future function expansion and improves the models’ processing speed. Additionally, it reduces the run time of the application and improves the system performance.

show abstract

Personal Privacy Protection via Irrelevant Faces Tracking and Pixelation in Video Live Streaming

Cited by 31 publications

References 51 publications

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

Enhancing Free-Living Fall Risk Assessment: Contextualizing Mobility Based IMU Data

"It Feels Like Being Locked in A Cage": Understanding Blind or Low Vision Streamers' Perceptions of Content Curation Algorithms

Intelligent Security Monitoring System Based on RISC-V SoC

Contact Info

Product

Resources

About