Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

Browse

Filters

20222

Advanced Search

Filter by

Settings

Subject: search.filters.subject.Detection

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    PublicationEmbargo
    Surveillance based Child Kidnap Detection and Prevention Assistance
    (IEEE, 2022-07-18) Kodikara, K. A. O. V; Hettiarachchi, P; Prathapa, D. M. J; Jayakody, J. M. A. M. S; Haddela, P. S
    This research paper is based on child kidnap detection and prevention to identify susceptible child kidnap by unauthorized persons. The intelligent surveillance system proposed for this is known as "AICare". The purpose behind developing a proper kidnap detection methodology is to enhance and strengthen the existing child security systems. The key is to identify the main characteristics of a kidnapper in real-time, which follows face recognition, speed detection and object detection theories. Face recognition is used to identify whether the outlined individual has covered his body, especially his face, to hide the true identity, or else the person's face is directly processed for authentication. Speed detection is helpful in calculating movement speed and capturing whether the targeted individual is moving in a hurry. Finally, the stranger is subjected to object detection in order to classify whether he/she is handling a sharp object or not. The captured outcomes are subjected to a decision tree to resolve the person as a kidnapper suspect. The system results in an overall accuracy that is above 90%. As this solution is child-sensitive and responsive, it provides a long term platform that can real-time monitor for potential kidnap based on the kidnapper characteristics to support the working from home parents to take care of their children during crucial times.
  • Thumbnail Image
    PublicationEmbargo
    Driving Through a Bend: Detection of Unsafe Driving Patterns and Prevention of Heavy Good Vehicle Rollovers
    (IEEE, 2022-01-17) Siriwardana, E. M. A. K; Piyawardana, V. S; Chandrasiri, S. S; Kaushalya, S. G. L. D. H; Sampath, K.D. Amila
    Road Traffic Crashes are simply ordinary within the present world. However, heavy goods vehicles (HGV) rollover has become a significant problem worldwide. Depending on the data collected, the sources used, and several key factors contribute to HGV overturning. Accidents overturn due to longer reaction time, shriveled driving performance, lack of driving experience, and driver carelessness. In further consideration, over-steering to turning over, not steering enough to stay in lane, over speed, high located center of gravity, weather condition, road condition, and the road's curves are the most contributing reasons to the overturning of a long vehicle. Thus, this paper proposes machine learning processes to overcome these problems and reduce the HGV rollovers. The proposed system includes a vehicle-equipped system and a ground-based operational surveillance camera. The Vehicle-equipped system can determine the safe speed at which the vehicle should travel according to the type of vehicle and curvature of the road and can detect road cracks and notify the driver by sending the notification to the vehicle dashboard screen. The ground-based driver support system can detect safe speed for HGVs and determine various other traffic parameters which can affect the HGV rollover accidents.