Learning of Scene-Specific Object Detectors by Classifier Co-Grids

Recently, classifier grids have shown to be a considerable alternative to sliding window approaches for object detection from static cameras. The main drawback of such methods is that they are biased by the initial model. In fact, the classifiers can be adapted to changing environmental conditions but due to conservative updates no new object-specific information is acquired. Thus, the goal of this work is to increase the recall of scene-specific classifiers while preserving their accuracy and speed. In particular, we introduce a co-training strategy for classifier grids using a robust on-line learner. Thus, the robustness is preserved while the recall can be increased. The co-training strategy robustly provides negative as well as positive updates. In addition, the number of negative updates can be drastically reduced, which additionally speeds up the system. In the experimental results these benefits are demonstrated on different publicly available surveillance benchmark data sets.