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

Recently, classifier grids have shown to be a considerablealternative to sliding window approaches for objectdetection from static cameras. The main drawback of suchmethods is that they are biased by the initial model. In fact,the classifiers can be adapted to changing environmentalconditions but due to conservative updates no new objectspecificinformation is acquired. Thus, the goal of this workis to increase the recall of scene-specific classifiers whilepreserving their accuracy and speed. In particular, we introducea co-training strategy for classifier grids using arobust on-line learner. Thus, the robustness is preservedwhile the recall can be increased. The co-training strategyrobustly provides negative as well as positive updates. Inaddition, the number of negative updates can be drasticallyreduced, which additionally speeds up the system. In theexperimental results these benefits are demonstrated on differentpublicly available surveillance benchmark data sets.