A Robust Adaptive Classifier for Detector Adaptation in a Video

Abstract

We propose a novel method for improved object detection in a video. Our approach adapts a generic offline trained detector (OTD) to a specific test video by collecting online samples in an unsupervised manner. Most of the existing adaptation methods focus on collecting confident online samples and do not address how to deal with ambiguous and noisy online samples. We address the importance of collecting online samples which are true representative of the actual objects present in the video and propose a Boosted Multiple Instance Random Fern (B-MIRF) classifier as the adaptive classifier. Multiple Instance Learning (MIL) provides reliability for training with noisy online samples and boosting process enables in obtaining more discriminative random ferns. We apply B-MIRF classifier on the detection responses obtained from OTD, hence our method improves the performance by improving the precision of OTD. We evaluate performance of our method on two challenging public datasets and show better performance than other state of the art methods.