By Miguel Lourenço, João Pedro Barreto (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)
The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed complaints of the twelfth eu convention on laptop imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers awarded have been rigorously reviewed and chosen from 1437 submissions. The papers are prepared in topical sections on geometry, second and 3D form, 3D reconstruction, visible acceptance and type, visible positive factors and picture matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and photograph registration, photometry: lighting fixtures and color, and snapshot segmentation.
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Additional resources for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part IV
These functions form an orthogonal basis of a subspace of L2 (Ω), space of square-integrable functions deﬁned on Ω. They have null values on the domain boundary. Let (an,m ) be the coeﬃcients of an,m φn,m (x, y). It comes: ξ in the basis (φn,m ): ξ(x, y) = n,m ϕ(x, y) = an,m φn,m (x, y) λ n,m n,m (11) and eq. (9) is veriﬁed: − Δϕ(x, y) = − an,m an,m Δφn,m (x, y) = λn,m φn,m (x, y) = ξ (12) λ λ n,m n,m n,m n,m At each date, having knowledge of ξ and (φn,m ), the values of (an,m ) are ﬁrst computed.
The experimental results demonstrate that our proposed method is robust in various benchmark scenarios. Keywords: Visual tracking, multiple features, transition probability matrix, robust likelihood function, tracker interaction, appearance learning. 1 Introduction Visual tracking is an important research topic in the field of computer vision because of its wide application in surveillance, robotics, human-computer interface, vehicle tracking, medical imaging, and so on. Due to the characteristics of the various vision applications, visual tracking is required to deal with practical challenges originating from dynamic circumstances such as object and/or background illumination changes, object pose variation, occlusions, and motion blur  as shown in Fig.
The overall framework and its components are specifically explained in Section 3. Experimental results are shown in Section 4 with the performance evaluation of the proposed tracking method and comparison with the state-of-the-art trackers. 2 Related Work During the last decade, many elaborate tracking frameworks have been proposed to achieve robust visual tracking by using multiple features [4–15]. Among these, some of studies that are closely most related to our approach are briefly explained in this section.