Recognition of Targets in Camera Networks
Fredrik Johansson · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2008
This thesis presents a re-recognition model for use in area camera network surveillance systems. The method relies on a mix of covariance matrix fea- ture descriptions and Bayesian networks for topological information. The system consists of an object recognition model and an re-recognition model. The object recognition model is responsible for separating people from the background and generating the position and description for each person and frame. This is done by using a foreground-background segmen- tation model to separate the background from a person. The segmented image is then tracked by a tracking algorithm that produces the coordinates for each person. It is also responsible for creating a silhouette that is used to create a feature vector consisting of a covariance matrix that describes the persons appearance. A hypothesis engine is then responsible for connecting the coordinates into a continues track that describes the trajectory were aa person has been visiting. Every trajectory is stored and available to the re-recognition model. It then compares two covariance matrices using a sophisticated distance me- thod to generate a probabilistic score value. The score is then combined with a likelihood-value of the topological match generated with a Bayesian network structure containing gathered statistical data. The topological in- formation is mainly intended to ¯lter the most un-likely matches.