SRI has developed a system to automatically analyze the Pattern of Life (PoL) of ports, routes and vessels from a large collection of AIS data. The PoL of these entities are characterized by a set of intuitive and easy to query semantic attributes. The prototype system provides an interface to ingest other types of information such as WAAS (Wide Area Aerial Surveillance) and GDELT (Global Database of Events, Language, and Tone) to augment knowledge of the Area of Operations. It can interact with users by answering questions and simulating what-if scenarios to keep human in the processing loop.
KEYWORDS: Video, Roads, Video surveillance, Detection and tracking algorithms, Surveillance, Target detection, Sensors, Data modeling, Geographic information systems, Databases
This paper presents a relational graph based approach to track thousands of vehicles from persistent wide area airborne
surveillance (WAAS) videos. Due to the low ground sampling distance and low frame rate, vehicles usually have small
size and may travel a long distance between consecutive frames, WAAS videos pose great challenges to correct
associate existing tracks with targets. In this paper, we explore road structure information to regulate both object based
vertex matching and pair-wise edge matching schemes in a relational graph. The proposed relational graph approach
then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized
association result. The experiments on hours of real WAAS videos demonstrate the relational graph matching framework
effectively improves vehicle tracking performance in large scale dense traffic scenarios.
In the intelligence community, aerial video has become one of the fastest growing data sources and it has been
extensively used in intelligence, surveillance, reconnaissance, tactical and security applications. This paper
presents a tracking approach to detect moving vehicles and person in such videos taken from aerial platform.
In our approach, we combine the layer segmentation approach with background stabilization and post-tracking
refinement to reliably detect small moving objects at the relatively low processing speed. For each individual
moving object, a corresponding layer is created to maintain an independent appearance and motion model
during the tracking process. After the online tracking process, we apply a post-tracking refinement process to
link the track fragments into a long consistent track ID to further reduce false alarm and increase detection rate.
Furthermore, a vehicle and person classifier is also integrated into the approach to identify the moving object
categories. The classifier is based on image histogram of gradient (HOG), which is more reliable to illumination
variation or camera automatic gain change. Finally, we report the results of our algorithms on a large scale of
EO and IR data set collected from VIVID program, and the results show that our approach achieved a good
and stable tracking performance on the data set that is more than eight hours.
Today, a large amount of videos is collected using aerial platforms. As the amount of aerial videos increases, there is an
urgent need for effective management and systematic exploitation of aerial videos. In this paper, we introduce an aerial
video management and exploitation system, named VideoQuest. The proposed system manages large-scale aerial video
database through automated video processing and content extraction. These processing and content extraction
algorithms include real-time video and metadata enhancement, hierarchical indexing and summarization, moving target
detection (i.e. MTI), moving object tracking and event detection. Additionally, VideoQuest allows user to interactively
search and browse large aerial video database based on sensor metadata and content extracted from aerial video. Using
the VideoQeust system, a user can search and retrieve mission-relevant information several magnitudes faster than
without using our system.
KEYWORDS: Video, Video surveillance, Databases, Cameras, Clouds, Sensors, Information visualization, Visualization, Geographic information systems, Video processing
Today, a large number of videos are collected using aerial platforms. These videos are used for various applications
from agriculture surveys to disaster response, from surveillance and security to intelligence gathering. As the amount of
aerial video increases, there is a need for systematic exploitation and effective management of the large aerial videos
database.
In this paper, we will introduce VideoQuest, an advanced aerial video exploitation and management system that
provides real-time aerial video enhancement, archiving, indexing and analysis capabilities such as sensor metadata
enhancement, moving target detection and tracking and event detection. To effectively and efficiently utilize archived
aerial videos, VideoQuest also provides spatial, temporal and content based indexing. To quickly retrieve videos in a
large-scale video database, the system summarizes aerial video hierarchically and based on content, such as objects,
tracks and events extracted from videos. Additionally, VideoQuest allows user to interactively search and browse A
large aerial video database through a "virtual fly control" GUI that dynamically assembles visual information according
to user's needs. Using the VideoQeust system, a user can search and retrieve mission-relevant information several
magnitudes faster than without using our system.
KEYWORDS: Image compression, Binary data, Computer programming, Video, Data compression, Video compression, Quantization, Statistical analysis, Video coding, Digital modulation
Correlation estimation plays a critical role in resource allocation and rate control for distributed data compression. A Wyner-Ziv encoder for distributed image compression is often considered as a lossy source encoder followed by a lossless Slepian-Wolf encoder. The source encoder consists of spatial transform, quantization, and bit plane extraction. In this work, we find that Gray code, which has been extensively used in digital modulation, is able to significantly improve the correlation between the source data and its side information. Theoretically, we analyze the behavior of Gray code within the context of distributed image compression. Using this theoretical model, we are able to efficiently allocate the bit budget and determine the code rate of the Slepian-Wolf encoder. Our experimental results demonstrate that the Gray code, coupled with accurate correlation estimation and rate control, significantly improves the picture quality, by up to 4 dB, over the existing methods for distributed image compression.
With the growth of digital video delivery, there is an increasing demand for better and more efficient ways to measure video quality. Most existing video quality metrics are reference-based approaches that are not suitable to measure the video quality perceived by the end user without access to reference videos. In this paper, we propose a reference-free video quality metric for MPEG coded videos. It predicts subjective quality ratings using both reference-free MPEG artifact measures and MPEG system parameters (known or estimated). The advantage of this approach is that it does not need a precise separation of content and artifact or the removal of any artifacts. By exploring the correlations among different artifacts and system parameters, our approach can remove content dependency and achieve an accurate estimate of the subjective ratings.
KEYWORDS: Digital watermarking, Forensic science, Visualization, Video, Computer security, Modulation, Internet, Linear filtering, Visual process modeling, Information security
Forensic digital watermarking is a promising tool in the fight
against piracy of copyrighted motion imagery content, but to
be effective it must be (1) imperceptibly embedded in high-definition motion picture source, (2) reliably retrieved, even from degraded copies as might result from camcorder capture and subsequent very-low-bitrate compression and distribution on the Internet, and (3) secure against unauthorized removal. No existing watermarking technology has yet to meet these three simultaneous requirements of fidelity, robustness, and security. We describe here a forensic watermarking approach that meets all three requirements. It is based on the inherent robustness and imperceptibility of very low spatiotemporal frequency watermark carriers, and on a watermark placement technique that renders jamming attacks too costly in picture quality, even if the attacker has complete knowledge of the embedding algorithm. The algorithm has been tested on HD Cinemascope source material exhibited in a digital cinema viewing room. The
watermark is imperceptible, yet recoverable after exhibition capture with camcorders, and after the introduction of other distortions such as low-pass filtering, noise addition, geometric shifts, and the manipulation of brightness and contrast.
In most existing color reproduction systems, color correction is performed in an open-looped fashion. For multiple generation color copying, color fidelity cannot be guaranteed as the errors introduced in color correction may accumulate. In this paper, we propose a method of solving the error accumulation problem by embedding color information as invisible digital watermark in hardcopies. When the hardcopy is scanned, the embedded information can be retrieved to provide real-time calibration. As the method is close-looped in nature, it may reduce error accumulation and improve color fidelity, particularly when copies go through multiple generation reproduction.
Effective document compression algorithms require scanned document images be first segmented into regions such as text, pictures and background. In this paper, we present a document compression algorithm that is based on the 3-layer (foreground/mask/background)MRC (mixture raster content) model. This compression algorithm first segments a scanned document image into different classes. Then, each class is transformed to the 3-layer MRC model differently according to the property of that class. Finally, the foreground and the back-ground layers are compressed using JPEG with customized quantization tables. The mask layer is compressed using JBIG2. The segmentation is optimized in the sense of rate distortion for the 3-layer MRC representation. It works in a closed loop fashion by a lying each transformation to each region of the document and then selecting the method that yields the best rate-distortion trade-off. The proposed segmentation algorithm can not only achieve a better rate-distortion trade-off, but also produce more robust segmentations by eliminating those mis-classifications which can cause severe artifacts. At similar bit rates, our MRC compression with the rate- distortion based segmentation can achieve a much higher subjective quality than state-of-the-art compression algorithms, such as JPEG and JPEG-2000.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.