CMRT09

Stilla, Uwe

Bibliographic data

Monograph

Persistent identifier:: 856955019

Author:: Stilla, Uwe

Title:: CMRT09

Sub title:: object extraction for 3D city models, road databases, and traffic monitoring ; concepts, algorithms and evaluation ; Paris, France, September 3 - 4, 2009 ; [joint conference of ISPRS working groups III/4 and III/5]

Scope:: X, 234 Seiten

Year of publication:: 2009

Place of publication:: Lemmer

Publisher of the original:: GITC

Identifier (digital):: 856955019

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: REFINING CORRECTNESS OF VEHICLE DETECTION AND TRACKING IN AERIAL IMAGE SEQUENCES BY MEANS OF VELOCITY AND TRAJECTORY EVALUATION D. Lenhart, S. Hinz

Document type:: Monograph

Structure type:: Chapter

CMRT09: Object Extraction for 3D City Models, Road Databases and Traffic Monitoring - Concepts, Algorithms, and Evaluation 184 A second function depicts a dense traffic situation at the same distance. The possibility for zero velocity shall be 0.5 (since jam cues more likely move slowly forward), while speeds of 10-30 shall be most likely and speeds larger than 50 simply impossible. By linear interpolation between the support points, this results in the functions depicted in Figure 3. Assuming that the possibilities evolve linearly over the dimension of density, we can derive 2D functions given a certain distance. The function for a position right in front of an intersection is shown in Figure 4. By linear interpolation along the third axis d, we receive a cubic membership function (Figure 5). u(v,D.d) Velocity v Figure 5: 3D membership function with slices at v=10, v=70, D=80, d=0 and d= 100 and its standard deviation: i (2) By applying a minimum threshold on the summed up weights of a section, we meet the circumstance that there are only false alarms in a free flow section. If the sum of the weights of a section is below the threshold, all detections of this section are removed. Finally, objects with a velocity v, < v — 2 • cr are regarded as outliers and eliminated. Then, a refined and unweighted average velocity is determined from the remaining detections. The resulting distribution is unbiased under the assumption that all false alarms have been eliminated. Figure 6: Velocity distribution and cutting-off criteria (red) per road section for each lane 4. RESULTS 3.3.2 Evaluation of velocity information Before the evaluation of the speed, the road is split into sections of length 50m and, near to intersections, of only 20m (sees Figure 6). Every detected object is assigned to one section and contributes to the section density. After the determination of the section density, the possibility /u A is derived from the above described 3D membership function for each object. The fuzzy possibility serves a weight in the calculation of a weighted average velocity for each section: Xva<( v i>A>4) " = 2>,(v„a,v) <0 / The concept has been tested on two different sets of image data so far. The results are shown in Figure 7. One image shows a highway section with free flowing traffic. Here, the detection was carried out by a blob detection algorithm as explained in (Lenhart et al.). In this case, the refinement was able to eliminate all false alarms and redundant objects that arose from the automatic detection. The second image shows a more complex scene with an urban highway section and an exit leading to an intersection with a traffic light. In this case, the detection has been carried out manually, however, considering a reasonable detection characteristic and quality. In this example, 12 objects have been correctly removed, leaving only one false alarm that could not be eliminated due to faulty tracking. In both examples, the correctness of the detection could be significantly increased by approximately 30%.

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