654 research outputs found
Artificial Intelligence Enabled Methods for Human Action Recognition using Surveillance Videos
Computer vision applications have been attracting researchers and academia. It is more so with cloud computing resources enabling such applications. Analysing video surveillance applications became an important research area due to its widespread applications. For instance, CCTV camera are used in public places in order to monitor situations, identify any theft or crime instances. In presence of thousands of such surveillance videos streaming simultaneously, manual analysis is very tedious and time consuming task. There is need for automated approach for analysis and giving notifications or findings to officers concerned. It is very useful to police and investigation agencies to ascertain facts, recover evidences and even exploit digital forensics. In this context, this paper throws light on different methods of human action recognition (HAR) using machine learning (ML) and deep learning (DL) that come under Artificial Intelligence (AI). It also reviews methods on privacy preserving action recognition and Generative Adversarial Networks (GANs). This paper also provides different datasets being used for human action recognition research besides giving an account of research gaps that help in pursuing further research in the area of human action recognition
Privacy-Preserving Action Recognition via Motion Difference Quantization
The widespread use of smart computer vision systems in our personal spaces
has led to an increased consciousness about the privacy and security risks that
these systems pose. On the one hand, we want these systems to assist in our
daily lives by understanding their surroundings, but on the other hand, we want
them to do so without capturing any sensitive information. Towards this
direction, this paper proposes a simple, yet robust privacy-preserving encoder
called BDQ for the task of privacy-preserving human action recognition that is
composed of three modules: Blur, Difference, and Quantization. First, the input
scene is passed to the Blur module to smoothen the edges. This is followed by
the Difference module to apply a pixel-wise intensity subtraction between
consecutive frames to highlight motion features and suppress obvious high-level
privacy attributes. Finally, the Quantization module is applied to the motion
difference frames to remove the low-level privacy attributes. The BDQ
parameters are optimized in an end-to-end fashion via adversarial training such
that it learns to allow action recognition attributes while inhibiting privacy
attributes. Our experiments on three benchmark datasets show that the proposed
encoder design can achieve state-of-the-art trade-off when compared with
previous works. Furthermore, we show that the trade-off achieved is at par with
the DVS sensor-based event cameras. Code available at:
https://github.com/suakaw/BDQ_PrivacyAR.Comment: ECCV 202
Visual Content Privacy Protection: A Survey
Vision is the most important sense for people, and it is also one of the main
ways of cognition. As a result, people tend to utilize visual content to
capture and share their life experiences, which greatly facilitates the
transfer of information. Meanwhile, it also increases the risk of privacy
violations, e.g., an image or video can reveal different kinds of
privacy-sensitive information. Researchers have been working continuously to
develop targeted privacy protection solutions, and there are several surveys to
summarize them from certain perspectives. However, these surveys are either
problem-driven, scenario-specific, or technology-specific, making it difficult
for them to summarize the existing solutions in a macroscopic way. In this
survey, a framework that encompasses various concerns and solutions for visual
privacy is proposed, which allows for a macro understanding of privacy concerns
from a comprehensive level. It is based on the fact that privacy concerns have
corresponding adversaries, and divides privacy protection into three
categories, based on computer vision (CV) adversary, based on human vision (HV)
adversary, and based on CV \& HV adversary. For each category, we analyze the
characteristics of the main approaches to privacy protection, and then
systematically review representative solutions. Open challenges and future
directions for visual privacy protection are also discussed.Comment: 24 pages, 13 figure
Text detection and recognition based on a lensless imaging system
Lensless cameras are characterized by several advantages (e.g.,
miniaturization, ease of manufacture, and low cost) as compared with
conventional cameras. However, they have not been extensively employed due to
their poor image clarity and low image resolution, especially for tasks that
have high requirements on image quality and details such as text detection and
text recognition. To address the problem, a framework of deep-learning-based
pipeline structure was built to recognize text with three steps from raw data
captured by employing lensless cameras. This pipeline structure consisted of
the lensless imaging model U-Net, the text detection model connectionist text
proposal network (CTPN), and the text recognition model convolutional recurrent
neural network (CRNN). Compared with the method focusing only on image
reconstruction, UNet in the pipeline was able to supplement the imaging details
by enhancing factors related to character categories in the reconstruction
process, so the textual information can be more effectively detected and
recognized by CTPN and CRNN with fewer artifacts and high-clarity reconstructed
lensless images. By performing experiments on datasets of different
complexities, the applicability to text detection and recognition on lensless
cameras was verified. This study reasonably demonstrates text detection and
recognition tasks in the lensless camera system,and develops a basic method for
novel applications
A review on visual privacy preservation techniques for active and assisted living
This paper reviews the state of the art in visual privacy protection techniques, with particular attention paid to techniques applicable to the field of Active and Assisted Living (AAL). A novel taxonomy with which state-of-the-art visual privacy protection methods can be classified is introduced. Perceptual obfuscation methods, a category in this taxonomy, is highlighted. These are a category of visual privacy preservation techniques, particularly relevant when considering scenarios that come under video-based AAL monitoring. Obfuscation against machine learning models is also explored. A high-level classification scheme of privacy by design, as defined by experts in privacy and data protection law, is connected to the proposed taxonomy of visual privacy preservation techniques. Finally, we note open questions that exist in the field and introduce the reader to some exciting avenues for future research in the area of visual privacy.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work is part of the visuAAL project on Privacy-Aware and Acceptable Video-Based Technologies and Services for Active and Assisted Living (https://www.visuaal-itn.eu/). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 861091. The authors would also like to acknowledge the contribution of COST Action CA19121 - GoodBrother, Network on Privacy-Aware Audio- and Video-Based Applications for Active and Assisted Living (https://goodbrother.eu/), supported by COST (European Cooperation in Science and Technology) (https://www.cost.eu/)
Dutkat: A Privacy-Preserving System for Automatic Catch Documentation and Illegal Activity Detection in the Fishing Industry
United Nations' Sustainable Development Goal 14 aims to conserve and sustainably use the oceans and their resources for the benefit of people and the planet. This includes protecting marine ecosystems, preventing pollution, and overfishing, and increasing scientific understanding of the oceans. Achieving this goal will help ensure the health and well-being of marine life and the millions of people who rely on the oceans for their livelihoods. In order to ensure sustainable fishing practices, it is important to have a system in place for automatic catch documentation.
This thesis presents our research on the design and development of Dutkat, a privacy-preserving, edge-based system for catch documentation and detection of illegal activities in the fishing industry. Utilising machine learning techniques, Dutkat can analyse large amounts of data and identify patterns that may indicate illegal activities such as overfishing or illegal discard of catch. Additionally, the system can assist in catch documentation by automating the process of identifying and counting fish species, thus reducing potential human error and increasing efficiency. Specifically, our research has consisted of the development of various components of the Dutkat system, evaluation through experimentation, exploration of existing data, and organization of machine learning competitions. We have also implemented it from a compliance-by-design perspective to ensure that the system is in compliance with data protection laws and regulations such as GDPR. Our goal with Dutkat is to promote sustainable fishing practices, which aligns with the Sustainable Development Goal 14, while simultaneously protecting the privacy and rights of fishing crews
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