A Low Complexity Video Watermarking in H.264 Compressed Domain

In this paper, a new blind and readable H.264 compressed domain watermarking scheme is proposed in which the embedding/extracting is performed using the syntactic elements of the compressed bit stream. As a result, it is not necessary to fully decode a compressed video stream both in the embedding and extracting processes. The method also presents an inexpensive spatiotemporal analysis that selects the appropriate submacroblocks for embedding, increasing watermark robustness while reducing its impact on visual quality. Meanwhile, the proposed method prevents bit-rate increase and restricts it within an acceptable limit by selecting appropriate quantized residuals for watermark insertion. Regarding watermarking demands such as imperceptibility, bit-rate control, and appropriate level of security, a priority matrix is defined which can be adjusted based on the application requirements. The resulted flexibility expands the usability of the proposed method.

Adolescent men's attitudes and decision-making in relation to an unplanned pregnancy. Responses to an interactive video drama

This study confronts a gender bias in research on adolescent pregnancy by exploring adolescent men’s decisions relating to a hypothetical unplanned pregnancy. A cross-sectional survey was conducted with adolescent men (N = 360) aged between 14 and 18 years attending schools in the Republic of Ireland. The study, the first of its kind in Europe, extends the small body of evidence on adolescent men and pregnancy decision-making by developing and examining reactions to an interactive video drama used in a comparable study in Australia. In addition, we tested a more comprehensive range of sociological and psychological determinants of adolescent men’s decisions regarding an unplanned pregnancy. Results showed that adolescent men were more likely to choose to keep the baby in preference to abortion or adoption. Adolescent men’s choice to continue the pregnancy (keep or adopt) in preference to abortion was significantly associated with anticipated feelings of regret in relation to abortion, perceived positive attitudes of own mother to keeping the baby and a feeling that a part of them might want a baby. Religiosity was also shown to underlie adolescent men’s views on the perceived consequences of an abortion in their lives.

Detection of aberrant behaviour in home environments from video sequence

We introduce an application for the detection of aberrant behaviour within home based environments, with a focus on repetitive actions, which may be present in instance of persons suffering from dementia. Video based analysis has been used to detect the motion of a person within a given scene in addition to tracking them over the time. Detection of repetitive actions has been based on the analysis of a person's trajectory using the principles of signal correlation. Along with the ability to detect repetitive motion the developed approach also has the ability to measure the amount of activity/inactivity within the scene during a given period of time. Our results showed that the developed approach had the ability to detect all patterns in the data set examined with an average accuracy of 96.67%. This work has therefore validated the proposed concept of video based analysis for the detection of repetitive activities.

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.