Camera calibration and vehicle tracking: Highway traffic video analytics

• Cost functions for matching objects at different times based on direction of travel, distance traveled, and speed.
• Graph-based tripartite (3D) Hungarian algorithm for tracking vehicles across time frames.
• Two vanishing points and width (VVW) method for camera calibration that is fully autonomous.
• Automatic determination of regions of interest and vehicle detection zones.
• Validation over varied weather conditions and compared with an alternative system.

We describe a real-time highway surveillance system (RHSS), which operates autonomously to collect statistics (speed and volume) and generates incident alerts (e.g., stopped vehicles). The system is designed to optimize long-term real-time performance accuracy. It also provides convenient integration to an existing surveillance infrastructure with different levels of service. Innovations include a novel 3-D Hungarian algorithm which is utilized for object tracking and a practical, hands-off mechanism for camera calibration. Speed is estimated based on trajectories after mapping/alignment with respect to dominant paths learned based on an evolutionary dynamics model. The system, RHSS, is intensively evaluated under different scenarios such as rain, low-contrast and high-contrast lightings. Performance is presented in comparison to a current commercial product. The contribution is innovation of new technologies that enable hands-off calibration (i.e., automatic detection of vanishing points) and improved accuracy (i.e., illumination balancing, tracking via a new 3-D Hungarian algorithm, and re-initialization of background detection on-the-fly). Results indicate the capability and applicability of the proposed system in real-time and real-world settings.