Abstract
The abrupt expansion of the Internet use over the last decade led to an uncontrollable amount of media stored in the Web. Image, video and news information has ooded the pool of data that is at our disposal and advanced data mining techniques need to be developed in order to take full advantage of them. The focus of this thesis is mainly on developing robust video analysis technologies concerned with detecting and recognizing activities in video. The work aims at developing a compact activity descriptor with low computational cost, which will be robust enough to discriminate easily among diverse activity classes. Additionally, we introduce a motion compensation algorithm which alleviates any issues introduced by moving camera and is used to create motion binary masks, referred to as compensated Activity Areas (cAA), where dense interest points are sampled. Motion and appearance descriptors invariant to scale and illumination changes are then computed around them and a thorough evaluation of their merit is carried out. The notion of Motion Boundaries Activity Areas (MBAA) is then introduced. The concept differs from cAA in terms of the area they focus on (ie human boundaries), reducing even more the computational cost of the activity descriptor. A novel algorithm that computes human trajectories, referred to as 'optimal trajectories', with variable temporal scale is introduced. It is based on the Statistical Sequential Change Detection (SSCD) algorithm, which allows dynamic segmentation of trajectories based on their motion pattern and facilitates their classification with better accuracy. Finally, we introduce an activity detection algorithm, which segments long duration videos in an accurate but computationally efficient manner. We advocate Statistical Sequential Boundary Detection (SSBD) method as a means of analysing motion patterns and report improvement over the State-of-the-Art.