@proceedings {3295, title = {Algebraic approach to recovering topological information in distributed camera networks.}, year = {2009}, month = {2009 Apr 13}, abstract = {

Camera networks are widely used for tasks such as surveillance, monitoring and tracking. In order to accomplish these tasks, knowledge of localization information such as camera locations and other geometric constraints about the environment (e.g. walls, rooms, and building layout) are typically considered to be essential. However, this information is not always required for many tasks such as estimating the topology of camera network coverage, or coordinate-free object tracking and navigation. In this paper, we propose a simplicial representation (called CN- complex) that can be constructed from discrete local observations from cameras, and utilize this novel representation to recover the topological information of the network coverage. We prove that our representation captures the correct topological information from network coverage for 2.5-D layouts, and demonstrate their utility in simulations as well as a real-world experimental set-up. Our proposed approach is particularly useful in the context of ad-hoc camera networks in indoor/outdoor urban environments with distributed but limited computational power and energy.

}, author = {Ahammad, Parvez and Lobaton, E and Sastry, S} } @conference {3298, title = {High speed action recognition and localization in compressed domain videos.}, booktitle = {IEEE Transactions on Circuits and Systems for Video Technology: Special issue on Video Surveillance}, year = {2008}, month = {2008 Aug}, abstract = {

We present a compressed domain scheme that is able to recognize and localize actions at high speeds. The recognition problem is posed as performing an action video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion directions and magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a benchmark action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our proposed method achieved a classification accuracy of 90\%, comparing favorably with existing methods in action classification accuracy, and is able to localize a template video of 80 x 64 pixels with 23 frames in a test video of 368 x 184 pixels with 835 frames in just 11 seconds, easily outperforming other methods in localization speed. We also perform a systematic investigation of the effects of various encoding options on our proposed approach. In particular, we present results on the compression-classification trade-off, which would provide valuable insight into jointly designing a system that performs video encoding at the camera front-end and action classification at the processing backend.

}, author = {Yeo, C and Ahammad, Parvez and Ramchandran, K and Sastry, S} } @proceedings {3300, title = {Multi-modal target tracking using heterogeneous sensor networks.}, year = {2008}, month = {2008 Aug 7}, abstract = {

The paper describes a target tracking system running on a Heterogeneous Sensor Network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

}, author = {Kushwaha, M and Amundson, I and Volgyesi, P and Ahammad, Parvez and Simon, G and Koutsoukos, X and Ledeczi, A and Sastry, S} } @proceedings {3303, title = {Comparative analysis of spatial patterns of gene expression in Drosophila Melanogaster imaginal discs.}, year = {2007}, month = {2007 Apr 21}, abstract = {

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

}, author = {Harmon, C and Ahammad, Parvez and Hammonds, Ann S and Weiszmann, Richard and Celniker, Susan E and Sastry, S and Rubin, Gerald M} } @proceedings {3302, title = {Unsupervised discovery of action hierarchies in large collections of activity videos.}, year = {2007}, month = {2007}, abstract = {

Given a large collection of videos containing activities, we investigate the problem of organizing it in an unsupervised fashion into a hierarchy based on the similarity of actions embedded in the videos. We use spatio-temporal volumes of filtered motion vectors to compute appearance-invariant action similarity measures efficiently - and use these similarity measures in hierarchical agglomerative clustering to organize videos into a hierarchy such that neighboring nodes contain similar actions. This naturally leads to a simple automatic scheme for selecting videos of representative actions (exemplars) from the database and for efficiently indexing the whole database. We compute a performance metric on the hierarchical structure to evaluate goodness of the estimated hierarchy, and show that this metric has potential for predicting the clustering performance of various joining criteria used in building hierarchies. Our results show that perceptually meaningful hierarchies can be constructed based on action similarities with minimal user supervision, while providing favorable clustering performance and retrieval performance.

}, author = {Ahammad, Parvez and Yeo, C and Ramchandran, K and Sastry, S} } @proceedings {3304, title = {Compressed domain real-time action recognition.}, year = {2006}, month = {2006 Oct 3}, abstract = {

We present a compressed domain scheme that is able to recognize and localize actions in real-time. The recognition problem is posed as performing a video query on a test video sequence. Our method is based on computing motion similarity using compressed domain features which can be extracted with low complexity. We introduce a novel motion correlation measure that takes into account differences in motion magnitudes. Our method is appearance invariant, requires no prior segmentation, alignment or stabilization, and is able to localize actions in both space and time. We evaluated our method on a large action video database consisting of 6 actions performed by 25 people under 3 different scenarios. Our classification results compare favorably with existing methods at only a fraction of their computational cost.

}, author = {Yeo, C and Ahammad, Parvez and Ramchandran, K and Sastry, S} } @proceedings {3305, title = {Joint nonparametric alignment for analyzing spatial gene expression patterns of Drosophila imaginal discs.}, volume = {2}, year = {2005}, month = {2005 Jun 20}, pages = {755-60}, abstract = {

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of congealing, while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

}, author = {Ahammad, Parvez and Harmon, C and Hammonds, Ann S and Sastry, S and Rubin, Gerald M} }