A system for airport surveillance: detection of people running, abandoned objects, and pointing gestures

The proposed system is focusing on the detection of three events in airport videos: a person running, a person putting down an object and a person pointing with his/her hand. The system was part of the NIST-TRECVid 2010 campaign, the training dataset consists in 100 hours of video from the Gatwick airport from five different cameras. For the detection of a person running, a non-parametric approach was adopted where statistics about tracked object velocities were accumulated over a long period of time using a Gaussian kernel. Outliers were then detected with the help of a kind of tstudent test taking into account the local statistics and the number of observations. For the detection of “object put” events, we follow a dual background segmentation approach where the difference in response between a short term and a long term background model (Mixture of Gaussians) triggers alerts. False alerts are excluded based on a simple modeling of the camera geometry in order to reject objects that are too large or too small given their positions in the image. The detection of pointing gesture events is based on the grouping of significant spatio-temporal corners (Harris) in a 3x3x3 cell called compound features as proposed recently by Andrew Gilbert et al. [10]. A hierarchical codebook is then derived from the training set based on a data mining algorithm looking for frequent items (called transactions). The algorithm was modified in order to deal with the large number of potential transactions (several millions) during the training step.