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Research & Integration


WP2: Evaluation, Integration and Standards
WP3: Visual Content Indexing
WP4: Content Description for Audio, Speech and Text
WP5: Multimodal Processing and Interaction
WP6: Machine Learning and Computation Applied to Multimedia
WP7: Dissemination towards Industry

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Detecting Falling People

Software developed by Enis Cetin's group at Bilkent University is capable of telling the difference between a person sitting down and one actually falling. This technology might prove very useful for intelligent surveillance of vulnerable groups such as patients, small children or todlers, and elderly persons. 

Detecting Falling People
Detecting Falling People

Summary: Detection of a falling person in an unsupervised area is a practical problem with applications in safety and security areas including supportive home environments and CCTV surveillance systems. We developed a falling person detection method using both audio and video information. It is difficult to distinguish a person simply sitting down on a floor from an uncontrolled stumbling and falling using only the video information. On the other hand, additional audio information provides a clear signal to differentiate the two actions. Human motion in video is modeled using Hidden Markov Models (HMM) in this paper. In addition, the audio track of the video is also used to distinguish a person simply sitting on a floor from a person stumbling and falling. Most video recording systems have the capability of recording audio as well and the impact sound of a falling person is also available as an additional clue. Audio channel data based decision is also reached using HMMs and fused with results of HMMs modeling the video data to reach a final decision.


  • Person falling (indoor scene) 
  • Person falling (outdoor scene)
  • Person collapsing (simulated heart attack)
  • Control experiment: person sitting down 
  • Contact: Enis Cetin (Bilkent University) 

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