In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.
|Number of pages||12|
|Journal||International Journal of Pattern Recognition and Artificial Intelligence|
|Publication status||Published - 2003 May|
Bibliographical noteFunding Information:
∗This research was supported Technology, Korea. †Author for correspondence.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Artificial Intelligence