Mobile and stationary computer vision based traffic surveillance techniques for advanced ITS applications.

摘要

This dissertation focuses on both mobile and stationary computer vision based traffic surveillance techniques, including the development of a new vision sensor, a survey and development of vision algorithms, as well as their applications in three different aspects of Intelligent Transportation System (ITS) areas with high quantitative requirements.;Portable Loop Fault Detection. In this dissertation, a stationary-vision based technique has been developed as part of a Portable Loop Fault Detection Tool (PLFDT). This work is complementary to recent research focusing on aggregated faulty loop data at a macroscopic level (the macroscopic level generally considers a large roadway network as consisting of links (roadways) and nodes (e.g., intersections)). The objectives of the PLFDT is to develop a real time, multi-lane, multi-vehicle tracking system for freeways using video cameras as the baseline measurement technique to compare the loop detection signal for direct fault detection for inductive loop system.;Localized Traffic Density Measurement. The embedded loop sensor system provides a direct measurement to traffic flow, roadway occupancy and average speed (only for double-loop detector). This type of sensor network does not directly measure traffic density; instead it can only be estimated. In this dissertation, we have developed systematic techniques to measure traffic conditions by utilizing both on- and off-board computer vision systems. We have developed a vision-equipped vehicle test bed for traffic surveillance purposes and have experimentally demonstrated the generation of localized traffic density from video processing and synthesizing. In contrast to the off-board surveillance systems (e.g. embedded loop sensor networks and stationary vision monitoring system), this type of on-board surveillance system provides a temporal- and spatial- continuous measurement of the localized traffic density. One of the key components developed is an Orthogonal Omni-directional Vision (OODV) System that has been developed to observe lane-level activity surrounding a vehicle, as well as the ability to observe the surrounding roadway geometry. This vision system uses a special catadioptric mirror providing a 360 degree orthogonal view of the environment.Based on this unique OODV, a roadway traffic surveillance system was designed and implemented. Combined with a GPS receiver that provides approximately 2 - 3 meters spatial resolution, this traffic surveillance system can be applied not only in several traffic applications which require localized traffic density/flow/average speed measurements, but also in some other applications that require detailed roadway geometry acquisition, and vehicle activity analysis. In order to have a better understanding of dynamic traffic conditions, we have incorporated this localized traffic density measurements into a Dynamic Roadway Network Database (DRND), which has been developed to fuse the roadway traffic data and the probe vehicle data.;Bicycle Safety Support System. In addition to these new traffic data collection/analysis techniques and verification process, computer vision techniques are being applied in safety studies as well. In the third part of this dissertation, stationary vision based observations have been made of the timing of bicyclists' intersection crossing maneuvers, to support of efforts in improving traffic signal timing to accommodate the needs of bicyclists. Video recordings were made of bicyclists' crossings and the video images were processed to extract the bicyclists' trajectories. These were synchronized with video images of the traffic signals so that the timing of the bicyclists' maneuvers could be determined relative to the signal phases. The processed data have yielded cumulative distributions of the crossing speeds of bicyclists who did not have to stop at the intersection and the start-up times and final crossing speeds of the bicyclists who had to cross from a standing start. This study provides a foundation in recommendation of minimal green signal time in terms of safety purpose. (Abstract shortened by UMI.)