Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features

Viewpoint invariant pedestrian recognition is an important yet under-addressed problem in computer vision. This is likely due to the difficulty in matching two objects with unknown viewpoint and pose. This paper presents a method of performing viewpoint invariant pedestrian recognition using an efficiently and intelligently designed object representation, the ensemble of localized features (ELF). Instead of designing a specific feature by hand to solve the problem, we define a feature space using our intuition about the problem and let a machine learning algorithm find the best representation. We show how both an object class specific representation and a discriminative recognition model can be learned using the AdaBoost algorithm. This approach allows many different kinds of simple features to be combined into a single similarity function. The method is evaluated using a viewpoint invariant pedestrian recognition dataset and the results are shown to be superior to all previous benchma...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Douglas Gray, Hai Tao
• Keywords: Class Specific Representation, Computer Vision, Discriminative Recognition Model, ECCV 2008, Invariant Pedestrian Recognition, Pedestrian Recognition Dataset, Unknown Viewpoint