Hossein Mousavi

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This paper presents a novel video descriptor, referred to as Histogram of Oriented Tracklets, for recognizing abnormal situation in crowded scenes. Unlike standard approaches that use optical flow, which estimates motion vectors only from two successive frames, we built our descriptor over long-range motion trajectories which is called tracklets in the(More)
Recently the histogram of oriented tracklets (HOT) was shown to be an efficient video representation for abnormality detection and achieved state-of-the-arts on the available datasets. Unlike standard video descriptors that mainly employ low level motion features, e.g. optical flow, the HOT descriptor simultaneously encodes magnitude and orientation of(More)
Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes(More)
Most of human actions consist of complex temporal compositions of more simple actions. Action recognition tasks usually relies on complex handcrafted structures as features to represent the human action model. Convolutional Neural Nets (CNN) have shown to be a powerful tool that eliminate the need for designing handcrafted features. Usually, the output of(More)
Abnormal detection in crowd is a challenging vision task due to the scarcity of real-world training examples and the lack of a clear definition of abnormality. To tackle these challenges, we propose a novel measure to capture the commotion of a crowd motion for the task of abnormality detection in crowd. The unsupervised nature of the proposed measure(More)
Oceanimonas sp. GK1 (IBRC-M 10197) is a marine halotolerant gammaproteobacterium which was characterized as producing large amounts of poly-β-hydroxybutyrate. Here we present the whole-genome sequence of Oceanimonas sp. GK1, which consists of a single circular chromosome of 3,514,537 bp and two plasmids 8,462 and 4,245 bp in length.
In this paper we introduce a novel method for general semantic segmentation that can benefit from general semantics of Convolutional Neural Network (CNN). Our segmentation proposes visually and semantically coherent image segments. We use binary encoding of CNN features to overcome the difficulty of the clustering on the high-dimensional CNN feature space.(More)
In crowd behavior studies, a model of crowd behavior needs to be trained using the information extracted from video sequences. Most of the previous methods are based on low-level visual features because there are only crowd behavior labels available as ground-truth information in crowd datasets. However, there is a huge semantic gap between low-level(More)