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The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years. The problem especially becomes challenging when the camera used to capture the video is dynamic.In this paper, we propose a statistical aggregation (SA) solution based on convolutional neural networks (CNNs) to address this problem.(More)
The task of classifying videos of natural dynamic scenes into appropriate classes has gained a lot of attention in recent years. The problem especially becomes challenging when the camera used to capture the video is dynamic. In this paper, we analyse the performance of statistical aggregation (SA) techniques on various pre-trained convolutional neural(More)
Analysis of a very long video and semantically describe the contents is a challenging task in computer vision. The present approaches such as video shot detection and summarization address this problem partially while maintaining the temporal coherency. To reduce the user efforts for seeing the whole video we have introduced a new technique which combines(More)
Kronecker-structured (K-S) models recently have been proposed for the efficient representation, processing, and classification of multidimensional signals such as images and video. Because they are tailored to the multi-dimensional structure of the target images, K-S models show improved performance in compression and reconstruction over more general (union(More)
In this paper, we are presenting a rotation variant Oriented Texture Curve (OTC) descriptor based mean shift algorithm for tracking an object in an unstructured crowd scene. The proposed algorithm works by first obtaining the OTC features for a manually selected object target, then a visual vocabulary is created by using all the OTC features of the target.(More)
Large datasets often have unreliable labels—such as those obtained from Amazon's Mechanical Turk or social media platforms—and classifiers trained on mislabeled datasets often exhibit poor performance. We present a simple, effective technique for accounting for label noise when training deep neural networks. We augment a standard deep network(More)
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