Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201

Wind-induced drift of objects at sea: the leeway field method

A method for conducting leeway field experiments to establish the drift properties of small objects (0.1-25 m) is described. The objective is to define a standardized and unambiguous procedure for condensing the drift properties down to a set of coefficients that may be incorporated into existing stochastic trajectory forecast models for drifting objects of concern to search and rescue operations and other activities involving vessels lost at sea such as containers with hazardous material. An operational definition of the slip or wind and wave-induced motion of a drifting object relative to the ambient current is proposed. This definition taken together with a strict adherence to 10 m wind speed allows us to refer unambiguously to the leeway of a drifting object. We recommend that all objects if possible be studied using what we term the direct method, where the object's leeway is studied directly using an attached current meter. We divide drifting objects into four categories, depending on their size. For the smaller objects (less than 0.5 m), an indirect method of measuring the object's motion relative to the ambient current must be used. For larger objects, direct measurement of the motion through the near-surface water masses is strongly recommended. Larger objects are categorized according to the ability to attach current meters and wind monitoring systems to them. The leeway field method proposed here is illustrated with results from field work where three objects were studied in their distress configuration; a 1:3.3 sized model of a 40-ft Shipping container, a World War II mine and a 220 l (55-gallon) oil drum.Comment: 33 pages, 12 figures, 3 table

Dynamic Denoising of Tracking Sequences

©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2008.920795In this paper, we describe an approach to the problem of simultaneously enhancing image sequences and tracking the objects of interest represented by the latter. The enhancement part of the algorithm is based on Bayesian wavelet denoising, which has been chosen due to its exceptional ability to incorporate diverse a priori information into the process of image recovery. In particular, we demonstrate that, in dynamic settings, useful statistical priors can come both from some reasonable assumptions on the properties of the image to be enhanced as well as from the images that have already been observed before the current scene. Using such priors forms the main contribution of the present paper which is the proposal of the dynamic denoising as a tool for simultaneously enhancing and tracking image sequences.Within the proposed framework, the previous observations of a dynamic scene are employed to enhance its present observation. The mechanism that allows the fusion of the information within successive image frames is Bayesian estimation, while transferring the useful information between the images is governed by a Kalman filter that is used for both prediction and estimation of the dynamics of tracked objects. Therefore, in this methodology, the processes of target tracking and image enhancement "collaborate" in an interlacing manner, rather than being applied separately. The dynamic denoising is demonstrated on several examples of SAR imagery. The results demonstrated in this paper indicate a number of advantages of the proposed dynamic denoising over "static" approaches, in which the tracking images are enhanced independently of each other