Multi-Camera Target Tracking in Blind Regions of Cameras with Non-overlapping Fields of View

In this paper, we propose a real time system for tracking targets across blind regions of multiple cameras with non-overlapping fields of views (FOVs) using camera topology, and targets’ motion and shape information. Kalman filters are used to robustly track each target’s shape and motion in each camera view and the common ground plane view composed of all camera views. The target’s track in the blind region between cameras is obtained using Kalman filter predictions. For multi-camera correspondence matching we compute the Gaussian distributions of the tracking parameters across cameras for the target motion and position in the ground plane view. Matching of targets across camera views uses a graph based track initialization scheme, which accumulates information from occurrences of target in several consecutive frames of the video. Probabilistic matching is carried out by using the track parameters for new tracks obtained from the graph in a camera view with the parameters of the terminated tracks learnt by Kalman filters in the other camera views and ground plane view. We obtain 85% accuracy for corresponding matching while tracking vehicles observed from two cameras monitoring a highway.