Multiresolution semantic activity characterisation and abnormality discovery in videos

This paper addresses the issue of activity understanding from video and its semantics-rich description. A novel approach is presented where activities are characterised and analysed at different resolutions. Semantic information is delivered according to the resolution at which the activity is observed. Furthermore, the multiresolution activity characterisation is exploited to detect abnormal activity. To achieve these system capabilities, the focus is given on context modelling by employing a soft computing-based algorithm which automatically enables the determination of the main activity zones of the observed scene by taking as input the trajectories of detected mobiles. Such areas are learnt at different resolutions (or granularities). In a second stage, learned zones are employed to extract people activities by relating mobile trajectories to the learned zones. In this way, the activity of a person can be summarised as the series of zones that the person has visited. Employing the inherent soft relation properties, the reported activities can be labelled with meaningful semantics. Depending on the granularity at which activity zones and mobile trajectories are considered, the semantic meaning of the activity shifts from broad interpretation to detailed description.Activity information at different resolutions is also employed to perform abnormal activity detection.

An annotation-free method for evaluating privacy protection techniques in videos

While several privacy protection techniques are pre- sented in the literature, they are not complemented with an established objective evaluation method for their assess- ment and comparison. This paper proposes an annotation- free evaluation method that assesses the two key aspects of privacy protection that are privacy and utility. Unlike some existing methods, the proposed method does not rely on the use of subjective judgements and does not assume a spe- cific target type in the image data. The privacy aspect is quantified as an appearance similarity and the utility aspect is measured as a structural similarity between the original raw image data and the privacy-protected image data. We performed an extensive experimentation using six challeng- ing datasets (including two new ones) to demonstrate the effectiveness of the evaluation method by providing a per- formance comparison of four state-of-the-art privacy pro- tection techniques.