Mademlis, Ioannis

Nikolaidis, Nikos

Pitas, Ioannis

Tefas, Anastasios

Summarization of videos depicting human activities is a timely problem with important applications, e.g., in the domains of surveillance or film/TV production, that steadily becomes more relevant. Research on video summarization has mainly relied on global clustering or local (frame-by-frame) saliency methods to provide automated algorithmic solutions for key-frame extraction. This work presents a method based on selecting as key-frames video frames able to optimally reconstruct the entire video. The novelty lies in modelling the reconstruction algebraically as a Column Subset Selection Problem (CSSP), resulting in extracting key-frames that correspond to elementary visual building blocks. The problem is formulated under an optimization frameworkand approximately solved via a genetic algorithm. The proposed video summarization method is being evaluated using a publicly availableannotated dataset and an objective evaluation metric. According to thequantitative results, it clearly outperforms the typical clustering approach

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Summarization of human activity videos via low-rank approximation

Ioannis Mademlis

Anastasios Tefas

Nikos Nikolaidis

Ioannis Pitas

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https://research-information.bris.ac.uk/files/104660171/Ioannis_Pitas_Summarization_Of_Human_Activity_Videos_Via_Low_Rank_Approximation.pdf

Summarization of human activity videos via low-rank approximation

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