129 research outputs found
Real-time Anomaly Detection and Localization in Crowded Scenes
In this paper, we propose a method for real-time anomaly
detection and localization in crowded scenes. Each video is
defined as a set of non-overlapping cubic patches, and is
described using two local and global descriptors. These
descriptors capture the video properties from different aspects.
By incorporating simple and cost-effective Gaussian
classifiers, we can distinguish normal activities and anomalies
in videos. The local and global features are based on
structure similarity between adjacent patches and the features
learned in an unsupervised way, using a sparse autoencoder.
Experimental results show that our algorithm is
comparable to a state-of-the-art procedure on UCSD ped2
and UMN benchmarks, but even more time-efficient. The
experiments confirm that our system can reliably detect and
localize anomalies as soon as they happen in a video
Person re-identification by robust canonical correlation analysis
Person re-identification is the task to match people in surveillance cameras at different time and location. Due to significant view and pose change across non-overlapping cameras, directly matching data from different views is a challenging issue to solve. In this letter, we propose a robust canonical correlation analysis (ROCCA) to match people from different views in a coherent subspace. Given a small training set as in most re-identification problems, direct application of canonical correlation analysis (CCA) may lead to poor performance due to the inaccuracy in estimating the data covariance matrices. The proposed ROCCA with shrinkage estimation and smoothing technique is simple to implement and can robustly estimate the data covariance matrices with limited training samples. Experimental results on two publicly available datasets show that the proposed ROCCA outperforms regularized CCA (RCCA), and achieves state-of-the-art matching results for person re-identification as compared to the most recent methods
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