Good Features to Correlate for Visual Tracking

During the recent years, correlation filters have shown dominant and spectacular results for visual object tracking. The types of the features that are employed in these family of trackers significantly affect the performance of visual tracking. The ultimate goal is to utilize robust features invariant to any kind of appearance change of the object, while predicting the object location as properly as in the case of no appearance change. As the deep learning based methods have emerged, the study of learning features for specific tasks has accelerated. For instance, discriminative visual tracking methods based on deep architectures have been studied with promising performance. Nevertheless, correlation filter based (CFB) trackers confine themselves to use the pre-trained networks which are trained for object classification problem. To this end, in this manuscript the problem of learning deep fully convolutional features for the CFB visual tracking is formulated. In order to learn the proposed model, a novel and efficient backpropagation algorithm is presented based on the loss function of the network. The proposed learning framework enables the network model to be flexible for a custom design. Moreover, it alleviates the dependency on the network trained for classification. Extensive performance analysis shows the efficacy of the proposed custom design in the CFB tracking framework. By fine-tuning the convolutional parts of a state-of-the-art network and integrating this model to a CFB tracker, which is the top performing one of VOT2016, 18% increase is achieved in terms of expected average overlap, and tracking failures are decreased by 25%, while maintaining the superiority over the state-of-the-art methods in OTB-2013 and OTB-2015 tracking datasets.Comment: Accepted version of IEEE Transactions on Image Processin

Nüvit Kodallı'ya onur madalyası

Taha Toros Arşivi, Dosya No: 328-Nevit Kodall

Efficient MRF Energy Propagation for Video Segmentation via Bilateral Filters

Segmentation of an object from a video is a challenging task in multimedia applications. Depending on the application, automatic or interactive methods are desired; however, regardless of the application type, efficient computation of video object segmentation is crucial for time-critical applications; specifically, mobile and interactive applications require near real-time efficiencies. In this paper, we address the problem of video segmentation from the perspective of efficiency. We initially redefine the problem of video object segmentation as the propagation of MRF energies along the temporal domain. For this purpose, a novel and efficient method is proposed to propagate MRF energies throughout the frames via bilateral filters without using any global texture, color or shape model. Recently presented bi-exponential filter is utilized for efficiency, whereas a novel technique is also developed to dynamically solve graph-cuts for varying, non-lattice graphs in general linear filtering scenario. These improvements are experimented for both automatic and interactive video segmentation scenarios. Moreover, in addition to the efficiency, segmentation quality is also tested both quantitatively and qualitatively. Indeed, for some challenging examples, significant time efficiency is observed without loss of segmentation quality.Comment: Multimedia, IEEE Transactions on (Volume:16, Issue: 5, Aug. 2014

Merhaba usta

Taha Toros Arşivi, Dosya Adı: Nazım Hikmetİstanbul Kalkınma Ajansı (TR10/14/YEN/0033) İstanbul Development Agency (TR10/14/YEN/0033

Pengaruh Film Marketing Mix Terhadap Kepuasan Audience Untuk Menonton Sekuel Film "X-Men : Days of Future Past"

In the world of marketing is the term marketing mix. Marketing mix is the key to success in achieving success. Marketing mix consists of product, price, place, and promotion. Product is everything that is offered to consumers. Viewing today, many films on offer, so it may be said that the film is a product. The film is currently circulating among people in the form of serialized story better known sequel. This sequel is in great demand by looking at the income earned enough to be "king" at the box office.This study aims to analyze the influence of the film marketing mix (script / genre film, actor/actress, age classification, and release strategy) to the satisfaction of the audience “X-Men” and the intentions of the audience. This study will be conducted by distributing questionnaires to 120 audience “X-Men”. The analysis technique used is the technique of quantitative analysis with multiple linear regression analysis method.The research results prove that there is a significant influence of the film marketing mix of audience satisfaction on the film “X-Men: Days Of Future Past”, but there were no significant effects of audience satisfaction of interest to watch the next film “X-Men”

Special issue on advances in three-dimensional television and video: Guest editorial

Cataloged from PDF version of article

Estimation of depth fields suitable for video compression using 3-D structures and motion of objects

Cataloged from PDF version of article.Intensity prediction along motion trajectories removes temporal redundancy considerably in video compression algorithms. In threedimensional (3-D) object-based video coding, both 3-D motion and depth values are required for temporal prediction. The required 3-D motion parameters for each object are found by the correspondence-based Ematrix method. The estimation of the correspondences—two-dimensional (2-D) motion field—between the frames and segmentation of the scene into objects are achieved simultaneously by minimizing a Gibbs energy. The depth field is estimated by jointly minimizing a defined distortion and bitrate criterion using the 3-D motion parameters. The resulting depth field is efficient in the rate-distortion sense. Bit-rate values corresponding to the lossless encoding of the resultant depth fields are obtained using predictive coding; prediction errors are encoded by a Lempel–Ziv algorithm. The results are satisfactory for real-life video scenes

3-D motion estimation of rigid objects for video coding applications using an improved iterative version of the E-matrix method

Cataloged from PDF version of article.As an alternative to current two-dimensional (2-D) motion models, a robust three-dimensional (3-D) motion estimation method is proposed to be utilized in object-based video coding applications. Since the popular E-matrix method is well known for its susceptibility to input errors, a performance indicator, which tests the validity of the estimated 3-D motion parameters both explicitly and implicitly, is defined. This indicator is utilized within the RANSAC method to obtain a robust set of 2-D motion correspondences which leads to better 3-D motion parameters for each object. The experimental results support the superiority of the proposed method over direct application of the E-matrix method

Rate-Distortion Efficient Piecewise Planar 3D Scene Representation from 2-D Images

Cataloged from PDF version of article.In any practical application of the 2-D-to-3-D conversion that involves storage and transmission, representation effi- ciency has an undisputable importance that is not reflected in the attention the topic received. In order to address this problem, a novel algorithm, which yields efficient 3-D representations in the rate distortion sense, is proposed. The algorithm utilizes two views of a scene to build a mesh-based representation incrementally, via adding new vertices, while minimizing a distortion measure. The experimental results indicate that, in scenes that can be approximated by planes, the proposed algorithm is superior to the dense depth map and, in some practical situations, to the block motion vector-based representations in the rate-distortion sense

Generalizable Embeddings with Cross-batch Metric Learning

Global average pooling (GAP) is a popular component in deep metric learning (DML) for aggregating features. Its effectiveness is often attributed to treating each feature vector as a distinct semantic entity and GAP as a combination of them. Albeit substantiated, such an explanation's algorithmic implications to learn generalizable entities to represent unseen classes, a crucial DML goal, remain unclear. To address this, we formulate GAP as a convex combination of learnable prototypes. We then show that the prototype learning can be expressed as a recursive process fitting a linear predictor to a batch of samples. Building on that perspective, we consider two batches of disjoint classes at each iteration and regularize the learning by expressing the samples of a batch with the prototypes that are fitted to the other batch. We validate our approach on 4 popular DML benchmarks.Comment: \c{opyright} 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work