アフィン部分空間のバッグによるロバスト物体追跡
Bags of Affine Subspaces for Robust Object Tracking
物体の見た目を連続フレームから構築したアフィン部分空間の集合としてモデル化し、候補領域も部分空間として表現してグラスマン多様体上の部分空間間距離で追跡する手法を提案した。遮蔽や変形、姿勢・照明変化を含む動画で高い追跡精度を示した。
著者: Sareh Shirazi, Conrad Sanderson, Chris McCool, Mehrtash T. Harandi
分類: cs.CV, cs.MM, cs.RO
原文アブストラクト
We propose an adaptive tracking algorithm where the object is modelled as a continuously updated bag of affine subspaces, with each subspace constructed from the object's appearance over several consecutive frames. In contrast to linear subspaces, affine subspaces explicitly model the origin of subspaces. Furthermore, instead of using a brittle point-to-subspace distance during the search for the object in a new frame, we propose to use a subspace-to-subspace distance by representing candidate image areas also as affine subspaces. Distances between subspaces are then obtained by exploiting the non-Euclidean geometry of Grassmann manifolds. Experiments on challenging videos (containing object occlusions, deformations, as well as variations in pose and illumination) indicate that the proposed method achieves higher tracking accuracy than several recent discriminative trackers.
