In this paper, the authors model vehicle detection and tracking in-car video based on motion.
This paper aims at real-time in-car video analysis to detect and track vehicles ahead for safety, autodriving, and target tracing. The study describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The researchers have investigated videos of day and night on different types of roads, showing that the approach is robust and effective in dealing with changes in environment and illumination and that real-time processing becomes possible for vehicle-borne cameras. The extracted geometry features from the video are continuously projected onto a 1-D profile and are constantly tracked. The researchers rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. The researchers probabilistically model the motion in the field of view according to the scene characteristic and the vehicle motion model. The hidden Markov model (HMM) is used to separate target vehicles from the background and track them probabilistically. (Published Abstract Provided)