Panic detection by regional velocity changes in crowded areas from surveillance video Güvenlik Kamerasi Görüntülerindeki Bölgesel Hiz Degisimlerini Degerlendirerek Kalabalik Ortamlarda Panik Durumu Tespiti

Husem H., KARSLIGİL M. E.

26th IEEE Signal Processing and Communications Applications Conference, SIU 2018, İzmir, Türkiye, 2 - 05 Mayıs 2018, ss.1-4, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu.2018.8404383
  • Basıldığı Şehir: İzmir
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.1-4
  • Anahtar Kelimeler: Convoltional Neural Networks, Panic Detection, Video Processing
  • İstanbul Gelişim Üniversitesi Adresli: Hayır

Özet

Common use of surveillance cameras and processing data gathered from them with only human power makes occur some difficulties about time and workload. In this work, a convolutional neural network based system is designed to detect panic situation with evaluating velocity changes of people by regions. Convolutional neural network for human detection; Kalman filter, Hungarian algorithm and convolutional neural network for extracting trajectory is used. The determination of the panic condition is made by evaluating the difference between the short and long time average of the speed of the people in each region. Unlike existing systems, by evaluating velocity changes on display locally, false positive results are reduced so that panic situation is detected more accurately.