Real-Time Pedestrian Detection using Eigenflow

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 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 performs this task in realtime. The system is also robust to small camera jitter and illumination changes. Moreover, we are able to detect moving children using the same system even though the training data is mainly composed of adult pedestrians.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Dhiraj Goel, Tsuhan Chen
• Keywords: AdaBoost Classifiers, Human Motion Patterns, ICIP 2007, Image Processing, Principal Component Analysis