Hot spot detection and classification in LWIR videos for person recognition

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 especially consider 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 AdaBoost are evaluated and extended to utilize the temporal information. We demonstrate that taking advantage of this temporal context can improve the classification performance.