We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. Then, it accomplishes the following tasks: 1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; 2) modeling paths commonly taken by objects across multiple camera views; and 3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learned, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22,951 and 14,985 trajectories, respectively.

We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. We assume that the topology of camera views is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are tracked in each of the camera views independently, and the positions and velocities of objects along trajectories are computed as features. Under a generative model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. It accomplishes two tasks: (1) grouping trajectories in different camera views belonging to the same activity into one cluster; (2) modeling paths commonly taken by objects across camera views. To our knowledge, no prior result of co-clustering trajectories in multiple camera views has been published. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and the learning is unsupervised. Our approach is evaluated on two very large data sets with 22, 951 and 14, 985 trajectories.

Understanding the dependency structure of a set of variables is a key component in various signal processing applications which involve data association. The simple task of detecting whether any dependency exists is particularly difficult when models of the data are unknown or difficult to characterize because of high-dimensional measurements. We review the use of nonparametric tests for characterizing dependency and how to carry out these tests with high-dimensional observations. In addition we present a method to assess the significance of the tests.

We present an approach for inferring the topology of a camera network by measuring statistical dependence between observations in different cameras. Two cameras are considered connected if objects seen departing in one camera is seen arriving in the other. This is captured by the degree of statistical dependence between the cameras. The nature of dependence is characterized by the distribution of observation transformations between cameras, such as departure to arrival transition times, and color appearance. We show how to measure statistical dependence when the correspondence between observations in different cameras is unknown. This is accomplished by non-parametric estimates of statistical dependence and Bayesian integration of the unknown correspondence. Our approach generalizes previous work which assumed restricted parametric transition distributions and only implicitly dealt with unknown correspondence. Results are shown on simulated and real data. We also describe a technique for learning the absolute locations of the cameras with Global Positioning System (GPS) side information.

This paper introduces a method for robustly estimating a planar tracking correspondence model (TCM) for a large camera network directly from tracking data and for employing said model to reliably track objects through multiple cameras. By exploiting the unique characteristics of tracking data, our method can reliably estimate a planar TCM in large environments covered by many cameras. It is robust to scenes with multiple simultaneously moving objects and limited visual overlap between the cameras. Our method introduces the capability of automatic calibration of large camera networks in which the topology of camera overlap is unknown and in which all cameras do not necessarily overlap. Quantitative results are shown for a five camera network in which the topology is not specified.

We present a model-based method for accurate extraction of pedestrian silhouettes from video sequences. Our approach is based on two assumptions, 1) there is a common appearance to all pedestrians, and 2) each individual looks like him/herself over a short amount of time. These assumptions allow us to learn pedestrian models that encompass both a pedestrian population appearance and the individual appearance variations. Using our models, we are able to produce pedestrian silhouettes that have fewer noise pixels and missing parts. We apply our silhouette extraction approach to the NIST gait data set and show that under the gait recognition task, our model-based silhouettes result in much higher recognition rates than silhouettes directly extracted from background subtraction, or any nonmodel-based smoothing schemes.

In [1] we introduced a linear statistical model of joint color changes in images due to variation in lighting and certain non-geometric camera pa- rameters. We did this by measuring the mappings of colors in one image of a scene to colors in another image of the same scene under different lighting conditions. Here we increase the flexibility of this color flow model by allowing flow coefficients to vary according to a low order polynomial over the image. This allows us to better fit smoothly vary- ing lighting conditions as well as curved surfaces without endowing our model with too much capacity. We show results on image matching and shadow removal and detection.

Recent work has shown impressive transform-invariant modeling and clustering for sets of images of objects with similar appearance. We seek to expand these capabilities to sets of images of an object class that show considerable variation across individual instances (e.g. pedestrian images) using a representation based on pixel-wise similarities, similarity templates. Because of its invariance to the colors of particular components of an object, this representation en- ables detection of instances of an object class and enables alignment of those instances. Further, this model implicitly represents the re- gions of color regularity in the class-speci(cid:12)c image set enabling a decomposition of that object class into component regions.

We present an approach for image retrieval using a very large number of highly selective features and efficient online learning. Our approach is predicated on the assumption that each image is generated by a sparse set of visual "causes" and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 45,000 highly selective features). At query time a user selects a few example images, and a technique known as "boosting" is used to learn a classification function in this feature space. By construction, the boosting procedure learns a simple classifier which only relies on 20 of the features. As a result a very large database of images can be scanned rapidly, perhaps a million images per second. Finally we will describe a set of experiments performed using our retrieval system on a database of 3000 images.