A novel multiplex cascade classifier for pedestrian detection

• A novel multiplex classifier model is proposed for pedestrian detection.
• A new fusion strategy is introduced by cascading classifiers.
• The weighted linear regression model is introduced to train the weak classifiers in our model.
• A structure table is introduced to label the foreground pixels by means of background differences.

Reliable pedestrian detection is of great importance in visual surveillance. In this paper, we propose a novel multiplex classifier model, which is composed of two multiplex cascades parts: Haar-like cascade classifier and shapelet cascade classifier. The Haar-like cascade classifier filters out most of irrelevant image background, while the shapelet cascade classifier detects intensively head-shoulder features. The weighted linear regression model is introduced to train its weak classifiers. We also introduce a structure table to label the foreground pixels by means of background differences. The experimental results illustrate that our classifier model provides satisfying detection accuracy. In particular, our detection approach can also perform well for low resolution and relatively complicated backgrounds.