@article {3294, title = {Coding of image feature descriptors for distributed rate-efficient visual correspondences.}, journal = {International Journal of Computer Vision}, volume = {94}, year = {2011}, month = {2011 Sep}, pages = {267-81}, abstract = {

Establishing visual correspondences is a critical step in many computer vision tasks involving multiple views of a scene. In a dynamic environment and when cameras are mobile, visual correspondences need to be updated on a recurring basis. At the same time, the use of wireless links between camera motes imposes tight rate constraints. This combination of issues motivates us to consider the problem of establishing visual correspondences in a distributed fashion between cameras operating under rate constraints. We propose a solution based on constructing distance preserving hashes using binarized random projections. By exploiting the fact that descriptors of regions in correspondence are highly correlated, we propose a novel use of distributed source coding via linear codes on the binary hashes to more efficiently exchange feature descriptors for establishing correspondences across multiple camera views. A systematic approach is used to evaluate rate vs visual correspondences retrieval performance; under a stringent matching criterion, our proposed methods demonstrate superior performance to a baseline scheme employing transform coding of descriptors.

}, issn = {0920-5691}, doi = {10.1007/s11263-011-0427-1}, author = {Ahammad, Parvez and Yeo, C and Ramchandran, K} } @proceedings {3296, title = {Rate-constrained distributed distance testing and its applications.}, year = {2009}, month = {2009}, abstract = {

We investigate a practical approach to solving one instantiation of a distributed hypothesis testing problem under severe rate constraints that shows up in a wide variety of applications such as camera calibration, biometric authentication and video hashing: given two distributed continuous-valued random sources, determine if they satisfy a certain Euclidean distance criterion. We show a way to convert the problem from continuous-valued to binary-valued using binarized random projections and obtain rate savings by applying a linear syndrome code. In finding visual correspondences, our approach uses just 49\% of the rate of scalar quantization to achieve the same level of retrieval performance. To perform video hashing, our approach requires only a hash rate of 0.0142 bpp to identify corresponding groups of pictures correctly.

}, author = {Yeo, C and Ahammad, Parvez and Zhang, H and Ramchandran, K} } @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 {3301, title = {A rate-efficient approach for establishing visual correspondences via distributed source coding.}, year = {2008}, month = {2008 Jan 28}, abstract = {

We consider the problem of communicating compact descriptors for the purpose of establishing visual correspondences between two cameras operating under rate constraints. Establishing visual correspondences is a critical step before other tasks such as camera calibration or object recognition can be performed in a network of cameras. We verify that descriptors of regions which are in correspondence are highly correlated, and propose the use of distributed source coding to reduce the bandwidth needed for transmitting descriptors required to establish correspondence. Our experiments demonstrate that the proposed scheme is able to provide compression gains of 57\% with minimal loss in the number of correctly established correspondences compared to a scheme that communicates the entire image of the scene losslessly in compressed form. Over a wide range of rates, the proposed scheme also provides superior performance when compared to simply transmitting all the feature descriptors.

}, author = {Yeo, C and Ahammad, Parvez and Ramchandran, K} } @proceedings {3297, title = {Rate-efficient visual correspondences using random projections.}, year = {2008}, month = {2008}, abstract = {

We consider the problem of establishing visual correspondences in a distributed and rate-efficient fashion by broadcasting compact descriptors. Establishing visual correspondences is a critical task before other vision tasks can be performed in a camera network. We use coarsely quantized random projections of descriptors to build binary hashes, and use the hamming distance between binary hashes as a matching criterion. In this work, we show that the hamming distance between the binary hashes has a binomial distribution, with parameters that are a function of the number of random projections and the euclidean distance between the original descriptors. We present experimental results that verify our result, and show that for the task of finding visual correspondences, sending binary hashes is more rate-efficient than prior approaches.

}, author = {Yeo, C and Ahammad, Parvez and Ramchandran, K} } @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} }