Current Issue : October - December Volume : 2012 Issue Number : 4 Articles : 4 Articles
Privacy is a big concern in current video surveillance systems. Due to privacy issues, many strategic places remain unmonitored\r\nleading to security threats. The main problem with existing privacy protection methods is that they assume availability of accurate\r\nregion of interest (RoI) detectors that can detect and hide the privacy sensitive regions such as faces. However, the current detectors\r\nare not fully reliable, leading to breaches in privacy protection. In this paper, we propose a privacy protection method that adopts\r\nadaptive data transformation involving the use of selective obfuscation and global operations to provide robust privacy even\r\nwith unreliable detectors. Further, there are many implicit privacy leakage channels that have not been considered by researchers\r\nfor privacy protection. We block both implicit and explicit channels of privacy leakage. Experimental results show that the\r\nproposed method incurs 38% less distortion of the information needed for surveillance in comparison to earlier methods of\r\nglobal transformation; while still providing near-zero privacy loss....
High-definition video streams� unique statistical characteristics and their high bandwidth requirements are considered to be a\r\nchallenge in both network scheduling and resource allocation fields. In this paper, we introduce an innovative way to model and\r\npredict high-definition (HD) video traces encoded with H.264/AVC encoding standard. Our results are based on our compilation\r\nof over 50HD video traces. We show that our model, simplified seasonal ARIMA (SAM), provides an accurate representation\r\nfor HD videos, and it provides significant improvements in prediction accuracy. Such accuracy is vital to provide better dynamic\r\nresource allocation for video traffic. In addition, we provide a statistical analysis of HD videos, including both factor and cluster\r\nanalysis to support a better understanding of video stream workload characteristics and their impact on network traffic.We discuss\r\nour methodology to collect and encode our collection of HD video traces. Our video collection, results, and tools are available for\r\nthe research community....
User-generated video content has grown tremendously fast to the point of outpacing professional content creation. In this work we\r\ndevelop methods that analyze contextual information of multiple user-generated videos in order to obtain semantic information\r\nabout public happenings (e.g., sport and live music events) being recorded in these videos. One of the key contributions of this\r\nwork is a joint utilization of different data modalities, including such captured by auxiliary sensors during the video recording\r\nperformed by each user. In particular, we analyze GPS data, magnetometer data, accelerometer data, video- and audio-content\r\ndata. We use these data modalities to infer information about the event being recorded, in terms of layout (e.g., stadium), genre,\r\nindoor versus outdoor scene, and the main area of interest of the event. Furthermore we propose a method that automatically\r\nidentifies the optimal set of cameras to be used in a multicamera video production. Finally, we detect the camera users which fall\r\nwithin the field of view of other cameras recording at the same public happening.We show that the proposed multimodal analysis\r\nmethods perform well on various recordings obtained in real sport events and live music performances...
The most significant bit- (MSB-) plane of an image is least likely to change by the most signal processing operations. This paper\r\npresents a novelmultibit logo-based signature, using the most significant gray-scale bits, which is then used to develop an extremely\r\nsimple but robust copyright protection scheme, where images along with their signatures are sent to a trusted third party when a\r\ndispute arises. Different ways of processing the MSB-plane before calculating the robust signature have been developed. This paper\r\nthen presents an innovative classifier-based technique to test the robustness and uniqueness of any signature-based scheme. A new\r\nMSB-based attack, which would defeat our scheme most, has also been proposed. Experimental results have clearly demonstrated\r\nthe superiority of the proposed scheme showing the high robustness of different MSB-based signatures over the existing signaturebased\r\nschemes....
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