| Paper: | TP-P3.9 |
| Session: | Image and Video Segmentation IV |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
ROBUST VEHICLE DETECTION THROUGH MULTIDIMENSIONAL CLASSIFICATION FOR ON BOARD VIDEO BASED SYSTEMS |
| Authors: |
Daniel Alonso; Grupo de Tratamiento de Imágenes - E.T.S. Ing. Telecomunicación | | |
| | Luis Salgado; Grupo de Tratamiento de Imágenes - E.T.S. Ing. Telecomunicación | | |
| | Marcos Nieto; Grupo de Tratamiento de Imágenes - E.T.S. Ing. Telecomunicación | | |
| Abstract: |
This paper presents a new in-vehicle real-time vehicle detection strategy which hypothesizes the presence of vehicles in rectangular sub-regions based on the robust classification of features vectors result of a combination of multiple morphological vehicle features: shadows, symmetry and corners. One vector is extracted for each region of the image likely containing vehicles as a multidimensional likelihood measure with respect to a simplified vehicle model. A supervised training phase set the representative vectors of the classes vehicle and non-vehicle, so that the hypothesis is verified or not according to the Mahalanobis distance between the feature vector and the representative vectors, performing the final vehicle segmentation. Excellent results have been obtained in several video sequences accurately detecting vehicles with very different aspect-ratio, color, size, etc, while minimizing the number of missing detections and false alarms. |
