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Surveillance and tracking systems often observe the same scene over extended time periods. When object motion is constrained by the scene (for instance, cars on roads, or pedestrians on sidewalks), it is advantageous to characterize and use scene-specific and location-specific priors to aid the tracking algorithm. This paper develops and demonstrates a method for creating priors for tracking that are conditioned on the current location of the object in the scene. These priors can be naturally incorporated in a number of tracking algorithms to make tracking more efficient and more accurate. We present a novel method to sample from these priors and show performance improvements (in both efficiency and accuracy) for two different tracking algorithms in two different problem domains.
Automating tools for geo-locating and geo-orienting static cameras is a key step in creating a useful global imaging network from cameras attached to the Internet. We present algorithms for partial camera calibration that rely on access to accurately time-stamped images captured over time from cameras that do not move. To support these algorithms we also offer a method of camera viewpoint change detection, or ``tamper detection'', which determines if a camera has moved in the challenging case when images are only captured every half hour. These algorithms are tested on a subset of the AMOS (Archive of Many Outdoor Scenes) database, and we present preliminary results that highlight the promise of these approaches.
Detecting, isolating, and tracking moving objects in an outdoor scene is a fundamental problem of visual surveillance. A key component of most approaches to this problem is the construction of a background model of intensity values. We propose extending background modeling to include learning a model of the expected shape of foreground objects. This paper describes our approach to shape description, shape space density estimation, and unsupervised model training. A key contribution is a description of properties of the joint distribution of object shape and image location. We show object segmentation and anomalous shape detection results on video captured from road intersections. Our results demonstrate the usefulness of building scene-specific and spatially-localized shape background models.
This paper details an empirical study of large image sets taken by static cameras. These images have consistent correlations over the entire image and over time scales of days to months. Simple second-order statistics of such image sets show vastly more structure than exists in generic natural images or video from moving cameras. Using a slight variant to PCA, we can decompose all cameras into comparable components and annotate images with respect to surface orientation, weather, and seasonal change. Experiments are based on a data set from 538 cameras across the United States which have collected more than 17 million images over the the last 6 months.
A key problem in widely distributed camera networks is geolocating the cameras. This paper considers three scenarios for camera localization: localizing a camera in an unknown environment, adding a new camera in a region with many other cameras, and localizing a camera by finding correlations with satellite imagery. We find that simple summary statistics (the time course of principal component coefficients) are sufficient to geolocate cameras without determining correspondences between cameras or explicitly reasoning about weather in the scene. We present results from a database of images from 538 cameras collected over the course of a year. We find that for cameras that remain stationary and for which we have accurate image timestamps, we can localize most cameras to within 50 miles of the known location. In addition, we demonstrate the use of a distributed camera network in the construction a map of weather conditions.
Manifold learning has become an important tool to characterize high-dimensional data that vary nonlinearly due to a few parameters. Applications to the analysis of medical imagery and human motion patterns have been successful despite the lack of effective tools to parameterize cyclic data sets. This paper offers an initial approach to this problem, and provides for a minimal parameterization of points that are drawn from cylindrical manifolds-data whose (unknown) generative model includes a cyclic and a non-cyclic parameter. Solving for this special case is important for a number of current, practical applications and provides a start toward a general approach to cyclic manifolds. We offer results on synthetic and real data sets and illustrate an application to de-noising cardiac ultrasound images.
In surveillance applications there may be multiple time scales at which it is important to monitor a scene. This work develops on-line, real-time algorithms that maintain background models simultaneously at many time scales. This creates a novel temporal de-composition of video sequence which can be used as a visualization tool for a human operator or an adaptive background model for classical anomaly detection and tracking algorithms. This paper solves the design problem for choosing appropriate time scales for the decomposition and derives the equations to approximately reconstruct the original video given only the temporal decompo-sition. We present two applications that highlight the potential of video processing; first a visualization tool that summarizes recent video behavior for a human operator in a single image, and second a pre-processing tool to detect "left bags" in the challenging PETS 2006 dataset which includes many occlusions of the left bag by pedestrians.
Search engine queries are normally brief but often return unmanageably long results, with which users struggle to determine document quality and relevance. In recent years, many studies have enhanced search results with metadata displayed as visual cues. Their success in helping users make faster and more accurate document judgments has been uneven, reflecting the wide range of information needs and document selection strategies of users, and also the relative effectiveness of different visualization forms. We identify the frequency with which query terms are found in a document as a straightforward and effective way for users to see the relationship between their query and the search results. In our prototype, we display these frequencies using simple pie charts. Despite performance limitations, evaluation with 101 users has been promising and suggests future developments.
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