3,144 research outputs found

    Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition

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    Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel Multiple View Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM) formulation combined with appearance information. Multiple stream 3D Convolutional Neural Networks (CNNs) are trained on the different views and time resolutions of the region adaptive Depth Motion Maps. Multiple views are synthesised to enhance the view invariance. The region adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information (RGB) are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multiple class Support Vector Machines (SVM)s. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human-object interaction. Three public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view actions and MSR 3D daily activity are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte

    Automatic Recognition of Film Genres

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    Film genres in digital video can be detected automatically. In a three-step approach we analyze first the syntactic properties of digital films: color statistics, cut detection, camera motion, object motion and audio. In a second step we use these statistics to derive at a more abstract level film style attributes such as camera panning and zooming, speech and music. These are distinguishing properties for film genres, e.g. newscasts vs. sports vs. commercials. In the third and final step we map the detected style attributes to film genres. Algorithms for the three steps are presented in detail, and we report on initial experience with real videos. It is our goal to automatically classify the large body of existing video for easier access in digital video-on-demand databases
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