45,634 research outputs found
Human Detection and Tracking for Video Surveillance A Cognitive Science Approach
With crimes on the rise all around the world, video surveillance is becoming
more important day by day. Due to the lack of human resources to monitor this
increasing number of cameras manually new computer vision algorithms to perform
lower and higher level tasks are being developed. We have developed a new
method incorporating the most acclaimed Histograms of Oriented Gradients the
theory of Visual Saliency and the saliency prediction model Deep Multi Level
Network to detect human beings in video sequences. Furthermore we implemented
the k Means algorithm to cluster the HOG feature vectors of the positively
detected windows and determined the path followed by a person in the video. We
achieved a detection precision of 83.11% and a recall of 41.27%. We obtained
these results 76.866 times faster than classification on normal images.Comment: ICCV 2017 Venice, Italy Pages 5 Figures
Survey of Object Detection Methods in Camouflaged Image
Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection
methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this
research paper authors presented survey of camouflage detection methods for different applications and areas
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
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