We present a fast and flexible content-based retrieval method for surveillance video. Designing a video search robust to uncertain activity duration, high variability in object shapes and scene content is challenging. We propose a two-step approach to video search. First, local motion features are inserted into an inverted index using locality-sensitive hashing (LSH). Second, we utilize a novel optimization approach based on edit distance to minimize temporal distortion, limited obscuration and imperfect queries. This approach assembles the local features stored in the index into a video segment which matches the query video. Pre-processing of archival video is performed in real-time, and retrieval speed scales as a function of the number of matches rather than video length. We demonstrate the effectiveness of the approach for counting, motion pattern recognition and abandoned object applications using a pair of challenging video datasets.

We present a fast and flexible content-based retrieval method for surveillance video. Designing a video search robust to uncertain activity duration, high variability in object shapes and scene content is challenging. We propose a two-step approach to video search. First, local features are inserted into an inverted index using locality-sensitive hashing (LSH). Second, we utilize a novel dynamic programming (DP) approach to robustify against temporal distortion, limited obscuration and imperfect queries. DP exploits causality to assemble the local features stored in the index into a video segment which matches the query video. Pre-processing of archival video is performed in real-time, and retrieval speed scales as a function of the number of matches rather than video length. We derive bounds on the rate of false positives, demonstrate the effectiveness of the approach for counting, motion pattern recognition and abandoned object applications using seven challenging video datasets and compare with existing work.

We present a method for estimating the amount of noise and blur in a distorted image. Our method is based on the multiscale structural similarity (MS-SSIM) framework that, although designed to measure image quality, is used to estimate the amount of blur and noise in a degraded image given a reference image. We show that there exists a bijective mapping between the 2-D noise/blur space and the 3-D MS-SSIM space, which allows to recover distortion parameters. That mapping allows to formulate the multidistortion-estimation problem as a classical optimization problem. Various search strategies such as Newton, simplex, NewUOA, and brute-force search are presented and compared. We also show that a bicubic patch can be used to approximate the bijective mapping between the noise/blur space and the 3-D MS-SSIM space. Interestingly, the use of such a patch reduces the processing time by a factor of 40 without significantly reducing precision. Based on quantitative results, we show that the amount of different types of blur and noise in a distorted image can be recovered with accuracy of roughly 2% and 8%, respectively. Our methods are compared with four state-of-the-art noise- and blur-estimation techniques.

In this paper, we present a method for estimating the amount of Gaussian noise and Gaussian blur in a distorted image. Our method is based on the MS-SSIM framework which, although designed to measure image quality, is used to estimate the amount of blur and noise in a degraded image given a reference image. Various search strategies such as Newton, Simplex, and brute force search are presented and rigorously compared. Based on quantitative results, we show that the amount of blur and noise in a distorted image can be recovered with an accuracy up to 0.95% and 5.40%, respectively. To our knowledge, such precision has never been achieved before.