Hierarchical and Incremental Event Learning Approach based on Concept Formation Models

International audienceWe propose an event learning approach for video, based on concept formation models. This approach incrementally learns on-line a hierarchy of states and event by aggregating the attribute values of tracked objects in the scene. The model can aggregate both numerical and symbolic values. The utilisation of symbolic attributes gives high flexibility to the approach. The approach also proposes the integration of attributes as a doublet value-reliability, for considering the effect in the event learning process of the incertainty inherited from previous phases of the video analysis process. Simultaneously, the approach recognises the states and events of the tracked objects, giving a multi-level description the object situation. The approach has been evaluated for an elderly care application and a rat behaviour analysis application. The results show that the approach is capable of learning and recognising meaningful events occurring in the scene, and to build a rich model of the objects behaviour. The results also show that the approach can give a description of the activities of a person (e.g. approaching to a table, crouching), and to detect abnormal events based on the frequency of occurrence

Data Mining in a Video Data Base

International audienceIn this chapter, we first present the state of the art in the domain. After we discuss how we achieve the pre-processing of the data. Then activity analysis and automatic classification is presented and finally we provide some results and evaluations.Dans ce chapitre, nous faisons un état de l'art. Ensuite nous montrons comment nous faisons le pre processind des data. Cette phase nous permet da faire l'analyse des activités et leur classification automatique. Après, quelques résultats et des évaluations sont présentés

Incremental learning algorithms and applications

International audienceIncremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years