Quantum mechanics

Algorithm

Histogram

Pixel

Norm (philosophy)

Rank (graph theory)

Eigenvalues and eigenvectors

Telecommunications

Computer science

Subtraction

Projection (relational algebra)

Physics

Pattern recognition (psychology)

Mathematics

Image (mathematics)

Political science

Feature vector

Artificial intelligence

Combinatorics

Matrix norm

Background subtraction

Computer vision

Arithmetic

Frame (networking)

Pattern Recognition

In this paper, a novel approach to abnormal event detection in crowded scenes is presented based on a new low-rank and compact coefficient dictionary learning (LRCCDL) algorithm. First, based on the background subtraction and binarization of surveillance videos, we construct a feature space by extracting the histogram of maximal optical flow projection (HMOFP) feature of the foreground from a normal training frame set. Second, in the training stage, a new joint optimization of the nuclear-norm and l2, 1-norm is applied to obtain a compact coefficient low-rank dictionary. Third, in the detection stage, l2, 1-norm optimization is utilized to obtain the reconstruction coefficient vectors of the testing samples. Note that the l2, 1-norm forces the reconstruction vectors of all the testing samples to compactly surround the same center in the training stage, such that the reconstruction errors of abnormal testing samples are different from those of normal ones. Finally, a reconstruction cost (RC) is introduced to detect abnormal frames. Experimental results on both global and local abnormal event detection show the effectiveness of our algorithm. Based on comparisons with state-of-the-art methods employing various criteria, the proposed algorithm achieves comparable detection results.

Abnormal event detection in surveillance videos based on low-rank and compact coefficient dictionary learning