Using scene features to improve wide-area video surveillance

We introduce two novel methods to improve the performance of wide area video surveillance applications by using scene features. First, we evaluate the drift in intrinsic and extrinsic parameters for typical pan-tilt-zoom (PTZ) cameras, which stems from accumulated mechanical and random errors after many hours of operation. When the PTZ camera is out of calibration, we show how the pose and internal parameters can be dynamically corrected by matching the scene features in the current image with a precomputed feature library. Experimental results show that the proposed method can keep a PTZ camera calibrated, even over long surveillance sequences. Second, we introduce a classifier to identify scene feature points, which can be used to improve robustness in tracking foreground objects and detect jitter in surveillance videos sequences. We show that the classifier produces improved performance on the problem of detecting counterflow in real surveillance video.