Traffic analysis with low frame rate camera networks

We propose a new traffic analysis framework using existing traffic camera networks. The framework integrates vehicle detection and image-based matching methods with geographic context to match vehicles across different views and analyze traffic. This is a challenging problem due to the low frame-rate of traffic-cams and the large distance between views. A vehicle may not always appear in a camera due to large inter-frame interval or inter-occlusion. We applied the proposed method to a traffic camera network to detect and track key vehicles to analyze traffic condition. Vehicles are detected using a multi-view approach. By integrating camera calibration information and GIS data, we extract traffic lane information and prior knowledge of expected vehicle orientation and image size at each image location. This improves detection speed and reduces false alarms by discarding unlikely scale and orientation. Subsequently, detected vehicles are matched across cameras using a view-invariant appearance model. For more accurate vehicle matching, traffic patterns observed at two sets of cameras are temporally aligned. Finally, key vehicles are globally tracked across cameras using the max-flow/min-cut network tracking algorithm. Traffic conditions at each camera location are presented on a map.