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Adaptive people detection based on cross-correlation maximization

Applying people detectors to unseen data is challenging since patterns distributions may significantly differ from the ones of the training dataset. In this paper, we propose a framework to adapt people detectors during runtime classification. Such adaptation takes advantage of multiple detectors to identify their best configurations (i.e. detection thresholds) without requiring manually labeled ground truth. We maximize the mutual information of detectors by pair-wise correlating their outputs to obtain a set of hypotheses for the detection thresholds. These hypotheses are later combined by weighted voting to obtain a final decision for the detection threshold of each detector. The proposed approach does not require re-training detectors and uses standard people detector outputs, i.e., bounding boxes, therefore it can employ various types of detectors. The experimental results demonstrate that the proposed approach outperforms state-of-the-art detectors whose optimal configuration is learned from training data.