Video processing and background subtraction for change detection and activity recognition.

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

DemCare action dataset for evaluating dementia patients in a home-based environment

Abstrac

Activity detection using sequential statistical boundary detection (SSBD)

The spiralling increase of video data has rendered the automated localization and recognition of activities an essential step for video content understanding. In this work, we introduce novel algorithms for detecting human activities in the spatial domain via a binary activity detection mask, the Motion Boundary Activity Area (MBAA), and in the time domain by a new approach, Statistical Sequential Boundary Detection (SSBD). MBAAs are estimated by analyzing the motion vectors using the Kurtosis metric, while dense trajectories are extracted and described using a low level HOGHOF descriptor and high level Fisher representation scheme, modeling a Support Vector Data Description (SVDD) hypersphere. SSBD is then realized by applying Sequential Change Detection with the Cumulative Sum (CUSUM) algorithm on the distances between Fisher data descriptors and the corresponding reference SVDD hyperspheres for rapid detection of changes in the activity pattern. Activities in the resulting video subsequences are then classified using an multi-class SVM model, leading to state of the art results. Our experiments with benchmark and real world data demonstrate that our technique is successful in reducing the computational cost and also in improving activity detection rates. (C) 2015 Elsevier Inc. All rights reserved

Efficient motion estimation methods for fast recognition of activities of daily living

This work proposes a framework for the efficient recognition of activities of daily living (ADLs), captured by static color cameras, applicable in real world scenarios. Our method reduces the computational cost of ADL recognition in both compressed and uncompressed domains by introducing system level improvements in State of-the-Art activity recognition methods. Faster motion estimation methods are employed to replace costly dense optical flow (OF) based motion estimation, through the use of fast block matching methods, as well as motion vectors, drawn directly from the compressed video domain (MPEG vectors). This results in increased computational efficiency, with minimal loss in terms of recognition accuracy. To prove the effectiveness of our approach, we provide an extensive, in-depthinvestigation of the trade-offs between computational cost, compression efficiency and recognition accuracy, tested on bench-mark and real-world ADL video datasets