We focus on the automatic detection and classification of players in a football match. Our approach is not based on any a priori knowledge of the outfits, but on the assumption that the two main uniforms detected correspond to the two football teams. The algorithm is designed to be able to operate in real time, once it has been trained, and is able to detect partially occluded players and update the color of the kits to cope with some gradual illumination changes through time. Our method, evaluated from real sequences, gave better detection and classification results than those obtained by a system using a manual selection of samples to compute a Gaussian mixture model.
We propose a complete application capable of tracking multiple objects in an environment monitored by multiple cameras. The system has been specially developed to be applied to sport games, and it has been evaluated in a real association-football stadium. Each target is tracked using a local importance-sampling particle filter in each camera, but the final estimation is made by combining information from the other cameras using a modified unscented Kalman filter algorithm. Multicamera integration enables us to compensate for bad measurements or occlusions in some cameras thanks to the other views it offers. The final algorithm results in a more accurate system with a lower failure rate.
In this paper we present an integrated system for face detection, tracking and recognition in complex scenes. The face detector is based on colour skin models, with adaptation to cope with non-stationary colour distributions over time. The face model is tracked along the sequence with a particle filter by comparing its colour histogram with the colour histogram of the sample position by means of the Bhattacharyya coefficient. Face identification is based on statistical deformable models, as Active Shape Models (ASM) and Active Appearance Models (AAM) for feature extraction and a multiclass Support Vector Machine as classifier. We have tested both models with a database of 100 faces verifying the best performance of the AAM model compared with the ASM model.