Deep learning algorithms are a subset of the machine learning algorithms, which aim at
discovering multiple levels of distributed representations. Recently, numerous deep learning …

With the success of deep learning in the field of computer vision, object recognition has
made important breakthroughs, and its recognition accuracy has been drastically improved …

Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …

The detection of anomalous behaviors in automated video surveillance is a recurrent topic in
recent computer vision research. Depending on the application field, anomalies can present …

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse
coding is presented. In addition to using class labels of training data, we also associate label …

Drastic variations in illumination across surveillance cameras make the person re-
identification problem extremely challenging. Current large scale re-identification datasets …

In this paper we propose a robust object tracking algorithm using a collaborative model. As
the main challenge for object tracking is to account for drastic appearance change, we …

In many large-scale video analysis scenarios, one is interested in localizing and recognizing
human activities that occur in short temporal intervals within long untrimmed videos. Current …

In this paper, we propose a new unsupervised spectral feature selection model by
embedding a graph regularizer into the framework of joint sparse regression for preserving …

Recently the sparse representation (or coding) based classification (SRC) has been
successfully used in face recognition. In SRC, the testing image is represented as a sparse …