Hamed Kiani Galoogahi

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Correlation filters take advantage of specific properties in the Fourier domain allowing them to be estimated efficiently: O(N D log D) in the frequency domain, versus O(D 3 + N D 2) spatially where D is signal length, and N is the number of signals. Recent extensions to correlation filters, such as MOSSE, have reignited interest of their use in the vision(More)
In this paper, we propose a new face descriptor to directly match face photos and sketches of different modalities, called Local Radon Binary Pattern (LRBP). LRBP is inspired by the fact that the shape of a face photo and its corresponding sketch is similar, even when the sketch is exaggerated by an artist. Therefore, the shape of face can be exploited to(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)
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)
In this work, we propose to employ multi-channel correlation filters for recognizing human actions (e.g. waking, riding) in videos. In our framework, each action sequence is represented as a multi-channel signal (frames) and the goal is to learn a multi-channel filter for each action class that produces a set of desired outputs when correlated with training(More)