The production of video has increased and expanded dramatically. There is a need to reach accurate video classification. In our work, we use deep learning as a mean to accelerate the video retrieval task by classifying them into categories. We classify a video depending on the text extracted from it. We trained our model using fastText, a library for efficient text classification and representation learning, and tested our model on 15000 videos. Experimental results show that our approach is efficient and has good performance. Our technique can be used on huge datasets. It produces a model that can be used to classify any video into a specific category very quickly.

This article focuses on video document comparison using audiovisual production invariants API!. API are characterized by invariant segments obtained on a set of low-level features. We propose an algorithm to detect production invariants throughout a collection of audiovisual documents. The algorithm runs on low-level features, considered as time series, and extracts invariant segments using a one-dimensional morphological envelop comparison. Then, based on the extracted results, we define a style similarity measure between two video documents. A derivative pseudo distance is also proposed.

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The purpose of our work is to automatically generate textual video description schemas from surveillance video scenes compatible with police incidents reports. Our proposed approach is based on a generic and flexible context-free ontology. The general schema is of the form [actuator] [action] [over/with] [actuated object] [+ descriptors: distance, speed, etc.]. We focus on scenes containing exactly two objects. Through elaborated steps, we generate a formatted textual description. We try to identify the existence of an interaction between the two objects, including remote interaction which does not involve physical contact and we point out when aggressivity took place in these cases. We use supervised deep learning to classify scenes into interaction or no-interaction classes and then into subclasses. The chosen descriptors used to represent subclasses are keys in surveillance systems that help generate live alerts and facilitate offline investigation.

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