Statistical and Spatial Consensus Collection for Detector Adaptation

The increasing interest in automatic adaptation of pedestrian detectors toward specific scenarios is motivated by the drop of performance of common detectors, especially in video-surveillance low resolution images. Different works have been recently proposed for unsupervised adaptation. However, most of these works do not completely solve the drifting problem: initial false positive target samples used for training can lead the model to drift. We propose to transform the outlier rejection problem in a weak classifier selection approach. A large set of weak classifiers are trained with random subsets of unsupervised target data and their performance is measured on a labeled source dataset. We can then select the most accurate classifiers in order to build an ensemble of weakly dependent detectors for the target domain. The experimental results we obtained on two benchmarks show that our system outperforms other pedestrian adaptation state-of-the-art methods.