Lagrangian-Convolutional Networks for Video Classification

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The amount of video data has rapidly increased throughout the last decades. This development rises the demand for effective methods to analyse video data beyond a single frame image-based analysis. Specifically the detection quantification and classification of dynamic motion information is a crucial aspect for processing time-dependent video data which yet needs to be fully exploited. With this project we request funding to develop a novel system for analysis and classification of video data based on a Lagrangian methodology. With our preliminary work we highlighted the innovative potential for video processing using such methods in combination with recent machine learning approaches. With this project we like to evolve and substantiate this approach and aim to develop a novel concept for video-level description and classification: the Lagrangian- Convolutional Neural Network (LaCNN). This concept takes advantage of recent in-depth understanding of motion signatures in a video sequence and exploits the capabilities of the Lagrangian methodology and the encoded motion information more effectively. In summary this novel concept will lead to a competitive effective and more transparent video classification system in comparison to existing state of the art methods.