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In this paper, we consider the challenging problem of anomalous behavior detection in videos. Considering the pixel based anomaly detection, we have used k-mean clustering, a posteriori probability based probabilistic model, and region intersection to detect the anomalies in the video. The proposed technique considers the normal events as the events of higher probabilities. Densely sampled points...
To understand the human action in still images, it is effective to detect the human region. However, since appearance of human is much different due to pose and occlusion, the detection is quite difficult. Here we propose robust human detection method to pose and occlusion using Bag-of-Words (BoW). In general, the location information is helpful in classification. When the human has occlusion and...
Affine transformation detection can be used in many computer vision and other applications. This paper presents a new method for affine transformation detection. The state-of-the-art methods are mainly divided into two classes. One class is based on complicated descriptors. But this kind of methods need a lot of time to establish and matching the complicated descriptors. The second class is based...
This paper explores the combining of powerful local texture descriptors and the advantages over single descriptors for texture classification. The proposed system is composed of three components: (i) highly discriminative and robust sorted random projections (SRP) features; (ii) a global Bag-of-Words (BoW) model; and (iii) the use of multiple kernel Support Vector Machines (SVMs) combining multiple...
This paper addresses the problem of object tracking by learning a discriminative classifier to separate the object from its background. The online-learned classifier is used to adaptively model object's appearance and its background. To solve the typical problem of erroneous training examples generated during tracking, an online multiple instance learning (MIL) algorithm is used by allowing false...
On-line boosting is one of the most successful on-line algorithms and thus applied in many computer vision applications. However, even though boosting, in general, is well known to be susceptible to class-label noise, on-line boosting is mostly applied to self-learning applications such as visual object tracking, where label-noise is an inherent problem. This paper studies the robustness of on-line...
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