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Recognizing dynamic scenes is one of the fundamental problems in sceneunderstanding, which categorizes moving scenes such as a forest fire,landslide, or avalanche. While existing methods focus on reliable capturing ofstatic and dynamic information, few works have explored frame selection from adynamic scene sequence. In this paper, we propose dynamic scene recognitionusing a deep dual descriptor based on `key frames' and `key segments.' Keyframes that reflect the feature distribution of the sequence with a smallnumber are used for capturing salient static appearances. Key segments, whichare captured from the area around each key frame, provide an additionaldiscriminative power by dynamic patterns within short time intervals. To thisend, two types of transferred convolutional neural network features are used inour approach. A fully connected layer is used to select the key frames and keysegments, while the convolutional layer is used to describe them. We conductedexperiments using public datasets as well as a new dataset comprised of 23dynamic scene classes with 10 videos per class. The evaluation resultsdemonstrated the state-of-the-art performance of the proposed method.

Recognizing Dynamic Scenes with Deep Dual Descriptor based on Key Frames and Key Segments