| Paper: | TA-P1.10 |
| Session: | Video Surveillance I / Document Image Processing & Analysis |
| Time: | Tuesday, September 18, 09:50 - 12:30 |
| Presentation: |
Poster
|
| Title: |
REAL-TIME PEDESTRIAN DETECTION USING EIGENFLOW |
| Authors: |
Dhiraj Goel; Carnegie Mellon University | | |
| | Tsuhan Chen; Carnegie Mellon University | | |
| Abstract: |
We propose a novel learning algorithm to detect moving pedestrians from a stationary camera in real-time. The algorithm learns a discriminative model based on eigenflow, i.e., the eigenvectors derived from applying Principal Component Analysis to the optical flow of moving objects, to differentiate between human motion patterns from other kind of motions like of cars etc. The learned model is a cascade of Adaboost classifiers of increasing complexity, with eigenflow vectors as the weak classifiers. Unlike some recent attempts to use motion for pedestrian detection, this system works in real-time. Moreover, the system is robust to small camera motion and slow illumination changes, and can detect moving children even though the training data had only adult pedestrians. |
