Behavioural Patterns Analysis of Low Entropy People Using Proximity Data

Over the years, wireless enabled mobile devices have become an important part of our daily activities that can provide rich contextual information about the location and environment of an individual (for example who is in your proximity? and where are you?). Advancement in technology has opened several horizons to analyse and model this contextual information for human behaviour understanding. Objective of this research work is to utilise this information from wireless proximity data to find repeated patterns in daily life activities and individual behaviours. These repeated patterns can give information about the unusual activities and behaviour of an individual. To validate and further investigate this concept, we used Bluetooth proximity data in this paper. Repeated activity patterns and behaviour of low entropy mobile people are detected by using two different techniques, N-gram and correlative matrix techniques. Primary purpose was to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Results have shown that these repeated patterns not only show short term daily routines but can also show the long term routines such as, monthly or yearly patterns in an individual’s daily life that can further help to analyse more complex and abnormal routines of human behaviour

A Framework to Recognise Daily Life Activities with Wireless Proximity and Object Usage Data

The profusion of wireless enabled mobile devices in daily life routine and advancement in pervasive computing has opened new horizons to analyse and model the contextual information. This contextual information (for example, proximity data and location information) can be very helpful in analysing the human behaviours. Wireless proximity data can provide important information about the behaviour and daily life routines of an individual. In this paper, we used Bluetooth proximity data to validate this concept by detecting repeated activity patterns and behaviour of low entropy mobile people by using n-gram and correlative matrix techniques. Primary purpose is to find out whether contextual information obtained from Bluetooth proximity data is useful for activities and behaviour detection of individuals. Repeated patterns found in Bluetooth proximity data can also show the long term routines such as, monthly or yearly patterns in an individual's daily life that can further help to analyse more complex and abnormal routines of human behaviour

Visual Security Evaluation Based on SIFT Object Recognition

Part 14: Image Video Processing 4International audienceThe paper presents a metric for visual security evaluation of encrypted images based on object recognition using the Scale Invariant Feature Transform (SIFT). The metrics’ behavior is demonstrated using three different encryption methods and its performance is compared to that of the PSNR, SSIM and Local Feature Based Visual Security Metric (LFBVSM). Superior correspondance to human perception and better responsiveness to subtle changes in visual security are observed for the new metric