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Accurate scale estimation of a target is a challenging research problem in visual object tracking. Most state-of-the-art methods employ an exhaustive scale search to estimate the target size. The exhaustive search strategy is computationally expensive and struggles when encountered with large scale variations. This paper investigates the problem of accurate and robust scale estimation in a tracking-by-detection framework. We propose a novel scale adaptive tracking approach by learning separate discriminative correlation filters for translation and scale estimation. The explicit scale filter is learned online using the target appearance sampled at a set of different scales. Contrary to standard approaches, our method directly learns the appearance change induced by variations in the target scale. Additionally, we investigate strategies to reduce the computational cost of our approach. Extensive experiments are performed on the OTB and the VOT2014 datasets. Compared to the standard exhaustive scale search, our approach achieves a gain of 2.5 percent in average overlap precision on the OTB dataset. Additionally, our method is computationally efficient, operating at a 50 percent higher frame rate compared to the exhaustive scale search. Our method obtains the top rank in performance by outperforming 19 state-of-the-art trackers on OTB and 37 state-of-the-art trackers on VOT2014. [ABSTRACT FROM PUBLISHER]

This paper presents a novel Bayesian formulation for aligning faces in unseen images. Our approach revisits the Constrained Local Models (CLM) formulation where an ensemble of local feature detectors are constrained to lie within the subspace spanned by a Point Distribution Model (PDM). Fitting such a model to an image typically involves two main steps: a local search using a detector, obtaining response maps for each landmark (likelihood term) and a global optimization that finds the PDM parameters that jointly maximize all the detections at once. The so-called global optimization can be posed as a Bayesian inference problem, where the posterior distribution of the shape (and pose) parameters can be inferred in a maximum a posteriori (MAP) sense. This work introduces an extended Bayesian global optimization strategy that includes two novel additions: (1) to perform second order updates of the PDM parameters (accounting for their covariance) and (2) to model the underlying dynamics of the shape variations, encoded in the prior term, by using recursive Bayesian estimation. Extensive evaluations were performed against state-of-the-art methods on several standard datasets (IMM, BioID, XM2VTS, LFW and FGNET Talking Face). Results show that the proposed approach significantly increases the fitting performance. [ABSTRACT FROM PUBLISHER]