Biblio

The electric network frequency (ENF) signal can be captured in multimedia recordings due to electromagnetic influences from the power grid at the time of recording. Recent work has exploited the ENF signals for forensic applications, such as authenticating and detecting forgery of ENF-containing multimedia signals, and inferring their time and location of creation. In this paper, we explore a new potential of ENF signals for automatic synchronization of audio and video. The ENF signal as a time-varying random process can be used as a timing fingerprint of multimedia signals. Synchronization of audio and video recordings can be achieved by aligning their embedded ENF signals. We demonstrate the proposed scheme with two applications: multi-view video synchronization and synchronization of historical audio recordings. The experimental results show the ENF based synchronization approach is effective, and has the potential to solve problems that are intractable by other existing methods.

This paper proposes content and network-aware redundancy allocation algorithms for channel coding and network coding to optimally deliver data and video multicast services over error prone wireless mesh networks. Each network node allocates redundancies for channel coding and network coding taking in to account the content properties, channel bandwidth and channel status to improve the end-to-end performance of data and video multicast applications. For data multicast applications, redundancies are allocated at each network node in such a way that the total amount of redundant bits transmitted is minimised. As for video multicast applications, redundancies are allocated considering the priority of video packets such that the probability of delivering high priority video packets is increased. This not only ensures the continuous playback of a video but also increases the received video quality. Simulation results for bandwidth sensitive data multicast applications exhibit up to 10× reduction of the required amount of redundant bits compared to reference schemes to achieve a 100% packet delivery ratio. Similarly, for delay sensitive video multicast applications, simulation results exhibit up to 3.5dB PSNR gains in the received video quality.

Recognizing activities in wide aerial/overhead imagery remains a challenging problem due in part to low-resolution video and cluttered scenes with a large number of moving objects. In the context of this research, we deal with two un-synchronized data sources collected in real-world operating scenarios: full-motion videos (FMV) and analyst call-outs (ACO) in the form of chat messages (voice-to-text) made by a human watching the streamed FMV from an aerial platform. We present a multi-source multi-modal activity/event recognition system for surveillance applications, consisting of: (1) detecting and tracking multiple dynamic targets from a moving platform, (2) representing FMV target tracks and chat messages as graphs of attributes, (3) associating FMV tracks and chat messages using a probabilistic graph-based matching approach, and (4) detecting spatial-temporal activity boundaries. We also present an activity pattern learning framework which uses the multi-source associated data as training to index a large archive of FMV videos. Finally, we describe a multi-intelligence user interface for querying an index of activities of interest (AOIs) by movement type and geo-location, and for playing-back a summary of associated text (ACO) and activity video segments of targets-of-interest (TOIs) (in both pixel and geo-coordinates). Such tools help the end-user to quickly search, browse, and prepare mission reports from multi-source data.

An abnormal behavior detection algorithm for surveillance is required to correctly identify the targets as being in a normal or chaotic movement. A model is developed here for this purpose. The uniqueness of this algorithm is the use of foreground detection with Gaussian mixture (FGMM) model before passing the video frames to optical flow model using Lucas-Kanade approach. Information of horizontal and vertical displacements and directions associated with each pixel for object of interest is extracted. These features are then fed to feed forward neural network for classification and simulation. The study is being conducted on the real time videos and some synthesized videos. Accuracy of method has been calculated by using the performance parameters for Neural Networks. In comparison of plain optical flow with this model, improved results have been obtained without noise. Classes are correctly identified with an overall performance equal to 3.4e-02 with & error percentage of 2.5.

We propose a method for analysis of surveillance video by using low rank and sparse decomposition (LRSD) with low latency combined with compressive sensing to segment the background and extract moving objects in a surveillance video. Video is acquired by compressive measurements, and the measurements are used to analyze the video by a low rank and sparse decomposition of a matrix. The low rank component represents the background, and the sparse component, which is obtained in a tight wavelet frame domain, is used to identify moving objects in the surveillance video. An important feature of the proposed low latency method is that the decomposition can be performed with a small number of video frames, which reduces latency in the reconstruction and makes it possible for real time processing of surveillance video. The low latency method is both justified theoretically and validated experimentally.

To reduce human efforts in browsing long surveillance videos, synopsis videos are proposed. Traditional synopsis video generation applying optimization on video tubes is very time consuming and infeasible for real-time online generation. This dilemma significantly reduces the feasibility of synopsis video generation in practical situations. To solve this problem, the synopsis video generation problem is formulated as a maximum a posteriori probability (MAP) estimation problem in this paper, where the positions and appearing frames of video objects are chronologically rearranged in real time without the need to know their complete trajectories. Moreover, a synopsis table is employed with MAP estimation to decide the temporal locations of the incoming foreground objects in the synopsis video without needing an optimization procedure. As a result, the computational complexity of the proposed video synopsis generation method can be significantly reduced. Furthermore, as it does not require prescreening the entire video, this approach can be applied on online streaming videos.

H.264/advanced video coding surveillance video encoders use the Skip mode specified by the standard to reduce bandwidth. They also use multiple frames as reference for motion-compensated prediction. In this paper, we propose two techniques to reduce the bandwidth and computational cost of static camera surveillance video encoders without affecting detection and recognition performance. A spatial sampler is proposed to sample pixels that are segmented using a Gaussian mixture model. Modified weight updates are derived for the parameters of the mixture model to reduce floating point computations. A storage pattern of the parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. The second contribution is a low computational cost algorithm to choose the reference frames. The proposed reference frame selection algorithm reduces the cost of coding uncovered background regions. We also study the number of reference frames required to achieve good coding efficiency. Distortion over foreground pixels is measured to quantify the performance of the proposed techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence.

Surveillance video streams monitoring is an important task that the surveillance operators usually carry out. The distribution of video surveillance facilities over multiple premises and the mobility of surveillance users requires that they are able to view surveillance video seamlessly from their mobile devices. In order to satisfy this requirement, we propose a cloud-based IPTV (Internet Protocol Television) solution that leverages the power of cloud infrastructure and the benefits of IPTV technology to seamlessly deliver surveillance video content on different client devices anytime and anywhere. The proposed mechanism also supports user-controlled frame rate adjustment of video streams and sharing of these streams with other users. In this paper, we describe the overall approach of this idea, address and identify key technical challenges for its practical implementation. In addition, initial experimental results were presented to justify the viability of the proposed cloud-based IPTV surveillance framework over the traditional IPTV surveillance approach.

Unmanned Aerial Systems (UAS) have raised a great concern on privacy recently. A practical method to protect privacy is needed for adopting UAS in civilian airspace. This paper examines the privacy policies, filtering strategies, existing techniques, then proposes a novel method based on the encrypted video stream and the cloud-based privacy servers. In this scheme, all video surveillance images are initially encrypted, then delivered to a privacy server. The privacy server decrypts the video using the shared key with the camera, and filters the image according to the privacy policy specified for the surveyed region. The sanitized video is delivered to the surveillance operator or anyone on the Internet who is authorized. In a larger system composed of multiple cameras and multiple privacy servers, the keys can be distributed using Kerberos protocol. With this method the privacy policy can be changed on demand in real-time and there is no need for a costly on-board processing unit. By utilizing the cloud-based servers, advanced image processing algorithms and new filtering algorithms can be applied immediately without upgrading the camera software. This method is cost-efficient and promotes video sharing among multiple subscribers, thus it can spur wide adoption.