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Privacy Protection in Video Surveillance Systems: Analysis of Subband-Adaptive Scrambling in JPEG XR
Hosik Sohn,De Neve, Wesley,Yong Man Ro IEEE 2011 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDE Vol.21 No.2
<P>This paper discusses a privacy-protected video surveillance system that makes use of JPEG extended range (JPEG XR). JPEG XR offers a low-complexity solution for the scalable coding of high-resolution images. To address privacy concerns, face regions are detected and scrambled in the transform domain, taking into account the quality and spatial scalability features of JPEG XR. Experiments were conducted to investigate the performance of our surveillance system, considering visual distortion, bit stream overhead, and security aspects. Our results demonstrate that subband-adaptive scrambling is able to conceal privacy-sensitive face regions with a feasible level of protection. In addition, our results show that subband-adaptive scrambling of face regions outperforms subband-adaptive scrambling of frames in terms of coding efficiency, except when low video bit rates are in use.</P>
Jae Young Choi,De Neve, Wesley,Ro, Y M,Plataniotis, K N IEEE 2010 IEEE transactions on circuits and systems for vide Vol.20 No.10
<P>In this paper, a novel face annotation framework is proposed that systematically leverages context information such as situation awareness information with current face recognition (FR) solutions. In particular, unsupervised situation and subject clustering techniques have been developed that are aided by context information. Situation clustering groups together photos that are similar in terms of capture time and visual content, allowing for the reliable use of visual context information during subject clustering. The aim of subject clustering is to merge multiple face images that belong to the same individual. To take advantage of the availability of multiple face images for a particular individual, we propose effective FR methods that are based on face information fusion strategies. The performance of the proposed annotation method has been evaluated using a variety of photo sets. The photo sets were constructed using 1385 photos from the MPEG-7 Visual Core Experiment 3 (VCE-3) data set and approximately 20000 photos collected from well-known photo-sharing websites. The reported experimental results show that the proposed face annotation method significantly outperforms traditional face annotation solutions at no additional computational cost, with accuracy gains of up to 25% for particular cases.</P>
Visually Weighted Neighbor Voting을 이용한 이미지 태그 정제 기술
이시형(Sihyoung Lee),Wesley De Neve,노용만(Yong Man Ro) 한국방송·미디어공학회 2011 한국방송공학회 학술발표대회 논문집 Vol.2011 No.7
온라인을 통한 이미지 공유는 사용자들이 활발하게 이용하고 있는 분야 중 하나이다. 사용자의 활발한 참여로 거대해진 이미지 데이터 베이스 내에서 효율적으로 이미지 검색을 수행하기 위해서는 이미지를 정확하기 표현하고 있는 태그의 존재가 매우 중요하다. 하지만, 최근 이미지에 등록 태그 중에서 상당 부분이 이미지와는 직접 관련이 없는 노이즈 태그라는 조사 결과는 노이즈 태그로 인해서 이미지 검색의 정확성이 저하될 수 있다는 가능성을 암시한다. 그래서 노이즈 태그를 효과적으로 구분하기 위해서는 태그의 종류에 적합한 태그 정제 기술을 도입할 필요가 있다. 본 연구는 이를 위해서 이미지의 시각적 유사도에 기반한 Visually weighted neighbor voting 방법을 제안했다. 이를 통해서 이미지와 태그 사이의 관련성을 효과적으로 측정할 수 있었다. 그리고 기존 기술보다 안정적으로 노이즈 태그를 구분할 수 있음을 실험을 통해서 증명하였다.
Seo, Jeong-Jik,Kim, Hyung-Il,De Neve, Wesley,Ro, Yong Man Butterworths 2017 Image and vision computing Vol.58 No.-
<P><B>Abstract</B></P> <P>Human action recognition (HAR) is a core technology for human–computer interaction and video understanding, attracting significant research and development attention in the field of computer vision. However, in uncontrolled environments, achieving effective HAR is still challenging, due to the widely varying nature of video content. In previous research efforts, trajectory-based video representations have been widely used for HAR. Although these approaches show state-of-the-art HAR performance for various datasets, issues like a high computational complexity and the presence of redundant trajectories still need to be addressed in order to solve the problem of real-world HAR. In this paper, we propose a novel method for HAR, integrating a technique for rejecting redundant trajectories that are mainly originating from camera movement, without degrading the effectiveness of HAR. Furthermore, in order to facilitate efficient optical flow estimation prior to trajectory extraction, we integrate a technique for dynamic frame skipping. As a result, we only make use of a small subset of the frames present in a video clip for optical flow estimation. Comparative experiments with five publicly available human action datasets show that the proposed method outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Removing redundant trajectories induced by camera movement </LI> <LI> A fast dynamic frame skipping technique for efficient feature extraction </LI> <LI> Consideration of both effectiveness and efficiency of human action recognition </LI> </UL> </P>
Parallel Deblocking Filtering in MPEG-4 AVC/H.264 on Massively Parallel Architectures
Pieters, Bart,Hollemeersch, Charles-Frederik J,De Cock, Jan,Lambert, Peter,De Neve, Wesley,Van de Walle, Rik IEEE 2011 IEEE transactions on circuits and systems for vide Vol.21 No.1
<P>The deblocking filter in the MPEG-4 AVC/H.264 standard is computationally complex because of its high content adaptivity, resulting in a significant number of data dependencies. These data dependencies interfere with parallel filtering of multiple macroblocks (MBs) on massively parallel architectures. In this letter, we introduce a novel MB partitioning scheme for concurrent deblocking in the MPEG-4 AVC/H.264 standard, based on our idea of deblocking filter independency, a corrected version of the limited error propagation effect proposed in the letter. Our proposed scheme enables concurrent MB deblocking of luma samples with limited synchronization effort, independently of slice configuration, and is compliant with the MPEG-4 H.264/AVC standard. We implemented the method on the massively parallel architecture of the graphics processing unit (GPU). Experimental results show that our GPU implementation achieves faster-than real-time deblocking at 1309 frames per second for 1080p video pictures. Both software-based deblocking filters and state-of-the-art GPU-enabled algorithms are outperformed in terms of speed by factors up to 10.2 and 19.5, respectively, for 1080p video pictures.</P>
안드로이드 모바일 플랫폼에서 이미지 태그 추천을 위한 시스템 구현
엄원용 ( Wonyong Eom ),민현석 ( Hyun-seok Min ),이시형 ( Sihyoung Lee ),( Wesley De Neve ),노용만 ( Yong Man Ro ) 한국정보처리학회 2010 한국정보처리학회 학술대회논문집 Vol.17 No.2
최근 스마트 폰을 이용한 사용자들이 생성하는 사진 데이터의 양이 급속히 증가하였다. 폭발적인 사진 데이터 양의 증가는 사용자가 원하는 사진에 대한 접근을 어렵게 하였다. 때문에 본 연구에서는 사진의 접근 및 관리의 효율을 높이기 위한 폭소노미를 통한 태그 추천 시스템을 안드로이드 모바일 플랫폼과 서버의 연계로 구현하였다. 구현된 애플리케이션은 25,000 장의 사진을 기반으로 하는 폭소노미를 통해 태그 추천을 하며, 태그 추천에 평균적으로 5.5 초의 시간이 걸렸다.