Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor are necessary, because of the typical limitations of single camera systems, resulting from occlusions by other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras will be done by a tracking approach. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system creates new derived and consolidated information of traffic participants. Thus, also descriptions of individual traffic participants are possible.

Video image detection systems (VIDS) are increasingly being used to monitor the traffic situation. Visual systems are operated with the help of image processing, which has aroused a permanent discussion concerning the quality of such systems. Therefore, different factors influencing the quality and reliability of such systems were examined within the last years. An automatic evaluation during 24 hours a day, 7 days a week has to be carried out to make an examination as comprehensive as possible. These kinds of examinations can be carried out using the “Urban Road Research Laboratory”, which is a specialised measurement section of the German Aerospace Center (DLR) Institute for Transportation Systems. An evaluation approach of the m3 system is described in this paper.

This paper discusses a trajectory based recognition algorithm to expand the common approaches for atypical event detection in multi-object traffic scenes and to obtain area-based types of information (e.g. maps of speed patterns, trajectory curvatures or erratic movements). Different views of the same area by more than one camera sensor are necessary, because of the typical limitations of single camera systems, resulting from occlusions by other cars, trees and traffic signs. Furthermore, distributed cooperative multi-camera system (MCS) enables a significant enlargement of the observation area. The fusion of object data from different cameras is done by a multi-target tracking approach. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. New and consolidated information of traffic participants is derived from the system.

In modern traffic management Video Image Detection System s (VIDS) are becoming increasingly important as traffic sensors. They are getting more affordable and don't require any road construction like commonly used induction loops. Furthermore, due to the fact that they are able to monitor a wide area, they potentially offer the derivation of a whole new set of traffic parameters. Good examples are the derivation of source-destination relations, queue-length, travel-times or general event detection like untypical movements, accidents, blockages and congestions. Additionally, by using more than one camera the surveillance area can be enlarged or the detection accuracy can be increased due to redundancy of observations. However, in order to take advantage of a multiple camera system, the observations from different cameras have to be fused. In the setup that will be presented a geometric fusion is proposed by projecting the observations into a combined geo-referenced coordinate frame. The basic requirement for this transformation is the knowledge of the interior and exterior orientation of every camera. Three different approaches for determining the exterior orientation have been implemented, namely a Newton method, a least squares adjustment based on ground control points and a method based on line features. Furthermore, direct linear transformation and minimum space resection are applied to calculate initial estimates. These algorithms are subject to an in depth evaluation in respect to their application as a traffic monitoring sensor.

Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor are necessary, because of the typical limitations of single camera systems, resulting from occlusions by other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras will be done by a tracking approach. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system creates new derived and consolidated information of traffic participants. Thus, also descriptions of individual traffic participants are possible.

Non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detectors. Video Image Detection Systems (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Existing VIDS emulate the inductive loops. We propose a trajectory based recognition algorithm to expand the common approach and to obtain new types of information (e.g. queue length or erratic movements). Different views of the same area by more than one camera sensor is necessary, because of the typical limitations of single camera systems, resulting from the occlusion effect of other cars, trees and traffic signs. A distributed cooperative multi-camera system enables a significant enlargement of the observation area. The trajectories are derived from multi-target tracking. The fusion of object data from different cameras is done using a tracking method. This approach opens up opportunities to identify and specify traffic objects, their location, speed and other characteristic object information. The system provides new derived and consolidated information about traffic participants. Thus, this approach is also beneficial for a description of individual traffic participants. Keywords: Multi-camera system, fixed-viewpoint camera, cooperative distributed vision, multi-camera orientation, multi-target tracking

The use of non-intrusive video-detection for traffic flow observation and surveillance is the primary alternative to conventional inductive loop detector. A Video Image Detection System (VIDS) can derive traffic parameters by means of image processing and pattern recognition methods. Classic systems like the inductive (double) loops or microwave radar detectors are able to measure presence and length of a vehicle but also speed and time gap to the preceding vehicle. Existing VIDS emulates the inductive loop by virtual loops and derives the traffic parameters in a similar way. Additional benefits of the VIDS, e.g. large area observation of traffic flow or detection of special behaviors of single vehicles can not be exploiting in this way. To expand the common approach also for new types of information (queue length or erratic movements), we use a trajectory based recognition algorithm, which is related to the detection and tracking of individual vehicles in an image sequence. It will be shown as an essential application for this algorithm how the usual characteristic traffic parameters can be derived from the measured trajectories again. A direct comparison between the video based and the classic inductive loop measurement of the traffic parameters is also possible.

In modern traffic management Video Image Detection Systems (VIDS) are becoming increasingly important as traffic sensors. They are getting more affordable and don’t require any road construction like commonly used induction loops. Furthermore, due to the fact that they are able to monitor a wide area, they potentially offer the derivation of a whole new set of traffic parameters. Good examples are the derivation of source-destination relations, queue-length, travel-times or general event detection like untypical movements, accidents, blockages and congestions. Additionally, by using more than one camera the surveillance area can be enlarged or the detection accuracy can be increased due to redundancy of observations. However, in order to take advantage of a multiple camera system, the observations from different cameras have to be fused. In the setup that will be presented a geometric fusion is proposed by projecting the observations into a combined geo-referenced coordinate frame. The basic requirement for this transformation is the knowledge of the interior and exterior orientation of every camera. Three different approaches for determining the exterior orientation have been implemented, namely a Newton method, a least squares adjustment based on ground control points and a method based on line features. Furthermore, direct linear transformation and minimum space resection are applied to calculate initial estimates. These algorithms are subject to an in depth evaluation in respect to their application as a traffic monitoring sensor.