Incremental learning for bootstrapping object classifier models

Abstract

Many state of the art object classification applications require many data samples, whose collection is usually a very costly process. Performing initial model training with synthetic samples (from virtual reality tools) has been proposed as a possible solution, although the resulting classification models need to be adapted (fine-tuned) to real-world data afterwards. For this bootstrapping process, we propose to use an incremental learning algorithm from the cognitive robotics domain which is particularly suited for perceptual problems. We apply it to a pedestrian detection problem where a synthetic dataset is used for initial training, and two different real-world datasets for fine-tuning and evaluation. The proposed scheme greatly reduces the number of real-world samples required while maintaining high classification accuracy. We additionally demonstrate several innovative incremental learning schemes for object detection, the basic idea being that usually only very few background samples are actually similar to pedestrian samples. By suitable arrangement of incremental learning steps, we can keep classification models simple by representing only such “hard” background samples.