Showing papers by "Pavel Korshunov published in 2012"

Subjective study of privacy filters in video surveillance

Abstract: Extensive adoption of video surveillance, affecting many aspects of the daily life, alarms the concerned public about the increasing invasion into personal privacy. Therefore, to address privacy issues, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks. In this paper, we propose a subjective evaluation methodology to analyze the tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. As an example, the proposed method is used to compare several commonly employed privacy protection techniques, such as blurring, pixelization, and masking applied to indoor surveillance video. The results show that, for the test material under analysis, the pixelization filter provides the best performance in terms of balance between privacy protection and intelligibility.

Crowdsourcing approach for evaluation of privacy filters in video surveillance

Abstract: Extensive adoption of video surveillance, affecting many aspects of the daily life, alarms the concerned public about the increasing invasion into personal privacy. To address these concerns, many tools have been proposed for protection of personal privacy in image and video. However, little is understood regarding the effectiveness of such tools and especially their impact on the underlying surveillance tasks. In this paper, we propose conducting a subjective evaluation using crowdsourcing to analyze the tradeoff between the preservation of privacy offered by these tools and the intelligibility of activities under video surveillance. As an example, the proposed method is used to compare several commonly employed privacy protection techniques, such as blurring, pixelization, and masking applied to indoor surveillance video. Facebook based crowdsourcing application was specifically developed to gather the subjective evaluation data. Based on more than one hundred participants, the evaluation results demonstrate that the pixelization filter provides the best performance in terms of balance between privacy protection and intelligibility. The results obtained with crowdsourcing application were compared with results of previous work using more conventional subjective tests showing that they are highly correlated.

A JPEG backward-compatible HDR image compression

Abstract: High Dynamic Range (HDR) imaging is expected to become one of the technologies that could shape next generation of consumer digital photography. Manufacturers are rolling out cameras and displays capable of capturing and rendering HDR images. The popularity and full public adoption of HDR content is however hindered by the lack of standards in evaluation of quality, file formats, and compression, as well as large legacy base of Low Dynamic Range (LDR) displays that are unable to render HDR. To facilitate wide spread of HDR usage, the backward compatibility of HDR technology with commonly used legacy image storage, rendering, and compression is necessary. Although many tone-mapping algorithms were developed for generating viewable LDR images from HDR content, there is no consensus on which algorithm to use and under which conditions. This paper, via a series of subjective evaluations, demonstrates the dependency of perceived quality of the tone-mapped LDR images on environmental parameters and image content. Based on the results of subjective tests, it proposes to extend JPEG file format, as the most popular image format, in a backward compatible manner to also deal with HDR pictures. To this end, the paper provides an architecture to achieve such backward compatibility with JPEG and demonstrates efficiency of a simple implementation of this framework when compared to the state of the art HDR image compression.

Evaluation of visual privacy filters impact on video surveillance intelligibility

Abstract: Since privacy issues are becoming important with growth of the video surveillance, many tools are proposed for protection of personal privacy in the video. However, little is understood regarding the effectiveness of such tools and their effect on the underlying surveillance tasks. In this paper, we propose a subjective evaluation methodology that compares several popular privacy protection techniques applied to typical indoor surveillance video. We identify and analyze the tradeoff between the privacy preservation of these tools and the intelligibility of activities in the resulted surveillance video.

Proposal for JPEG backward compatible HDR image compression

Abstract: Keywords: High dynamic range ; HDR ; backward compatibility ; JPEG ; subjective tests ; tone-mapping Reference EPFL-WORKING-185948 Record created on 2013-04-13, modified on 2017-05-10

MediaEval 2012 Visual Privacy Task: Applying Transform-domain Scrambling to Automatically Detected Faces

Abstract: In this paper, we describe our approach and discuss evaluation results for the MediaEval 2012 Visual Privacy task. The goal of the task is to obscure faces of people visible in provided surveillance clips to preserve their personal privacy. We also additionally assume, although it is not explicitly stated in the task description, that the privacy protection should be done in an automated way and the applied privacy tool should be reversible and prone to attacks. We use a combination of a face detection algorithm and transform-domain scrambling technique, which pseudo-randomly scrambles bits during encoding, that was applied to the detected face regions. The evaluations of the resulted automated privacy protection tool showed that inaccuracies of the face detection algorithm affected both objective and subjective results. An interesting finding is how- ever that scrambling, while being non-distractive to the evaluating subjects, appeared significantly irritating with score of 0.8, but only for ’evening’ subset of the dataset.