Hot Spot Detection and Classification in LWIR Videos for Person Recognition

Conference paper


Michael Teutsch
Thomas Müller


Firooz A. Sadjadi, Abhijit Mahalanobis (eds.), Automatic Target Recognition XXIII, Proceedings of SPIE Vol. 8744, 2013.


SPIE Defense, Security, and Sensing: Automatic Target Recognition XXIII, Baltimore, MD, USA, April 29 - May 3, 2013

Person recognition is a key issue in visual surveillance. It is needed in many security applications such as intruder detection in military camps but also for gaining situational awareness in a variety of different safety applications. A solution for LWIR videos coming from a moving camera is presented that is based on hot spot classification to distinguish persons from background clutter and other objects. We focus on objects in higher distance with small appearance in the image. Hot spots are detected and tracked along the videos. Various image features are extracted from the spots and different classifiers such as SVM or Boosting are evaluated and extended to utilize the temporal information. We demonstrate that considering this temporal context can improve the classification performance.