Subspace-Based Adaptation of Detectors for Video

Abstract

Object detection in videos has always been a challenging problem to work with. Detection of a particular class object plays an important role in many real-world applications. Since the domain of source and target video vary significantly, classifier being trained on source video does not give expected results on the target video. Thus, domain adaptation techniques are used, one of which is Subspace Based Adaptation. In this technique, first, we compute both source and target subspace from the features collected. Since we do not have target data directly, we use different ways to get data from the target video. Compute subspace after collecting the data from both source and target videos. Eigen vectors describe this generated source and target subspaces.