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We introduce a tracking-by-detection method that integrates a deep object detector with a particle filter tracker under the regularization framework where the tracked object is represented by a sparse dictionary. A novel observation model which establishes consensus between the detector and tracker is formulated that enables us to update the dictionary with the guidance of the deep detector. This yields an efficient representation of the object appearance through the video sequence hence improves robustness to occlusion and pose changes. The proposed tracker employs a 7D affine state vector formulated to output deformed object bounding boxes that significantly increases robustness to scale changes. Performance evaluation has been carried out on a subset of challenging VOT2016 and VOT2018 benchmarking video sequences for 80 object classes of COCO. Numerical results demonstrate that the introduced tracker, L1DPF-M, achieves comparable robustness while it outperforms state-of-the-art trackers in success rate where the improvement achieved at IoU-th = 0.5 on the used VOT2016 and VOT2018 sequences is 11% and 9%, respectively. [ABSTRACT FROM AUTHOR]

We propose a method based on perceptual prosodic features for medium term speaker state classification, particularly sleepiness detection. Unlike existing methods, our features represent spectral characteristics of speech in perceptual bands and also track temporal content omitting any linguistic segmentation. Despite conventional methods, we aim to model transitions between non-sleepy and sleepy modes rather than emotional states. Along with the proposed compact feature set, the developed system enable discrimination of medium term speaker states with a lower complexity compared to existing systems. This is achieved by constructing a dictionary for speech data based on bag-of-words concept. It has been identified that a training setup which is based on learned codewords, yields a robust classifier for sleepy speech. The speaker state classification has been performed by applying a two-class classification scheme on the observed test data. The numerical results, obtained on the Sleepy Language Corpus (SLC) by using Support Vector Machines (SVM) classifier, demonstrate a 10% improvement on average on unweighted recall rates compared to the benchmarking results. The introduced method is promising for online applications because of its frame based feature extraction scheme which differs from conventional segmental descriptor extraction techniques. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce an incremental non-negative matrix factorization (INMF) scheme in order to overcome the difficulties that conventional NMF has in online processing of large data sets. The proposed scheme enables incrementally updating its factors by reflecting the influence of each observation on the factorization appropriately. This is achieved via a weighted cost function which also allows controlling the memorylessness of the factorization. Unlike conventional NMF, with its incremental nature and weighted cost function the INMF scheme successfully utilizes adaptability to dynamic data content changes with a lower computational complexity. Test results reported for two video applications, namely background modeling in video surveillance and clustering, demonstrate that INMF is capable of online representing data content while reducing dimension significantly. [Copyright &y& Elsevier]

This paper introduces two spatial methods in order to embed watermark data into fingerprint images, without corrupting their features. The first method inserts watermark data after feature extraction, thus preventing watermarking of regions used for fingerprint classification. The method utilizes an image adaptive strength adjustment technique which results in watermarks with low visibility. The second method introduces a feature adaptive watermarking technique for fingerprints, thus applicable before feature extraction. For both of the methods, decoding does not require original fingerprint image. Unlike most of the published spatial watermarking methods, the proposed methods provide high decoding accuracy for fingerprint images. High data hiding and decoding performance for color images is also observed. [Copyright &y& Elsevier]

Summary As the retransmission mechanism of the Session Initiation Protocol (SIP) significantly increases the amount of incoming traffic and makes an overloaded SIP server unstable, the models created for a stable SIP system cannot be effectively used to evaluate the performance of the overloaded server. To overcome this difficulty, in this paper, we present a fluid-flow model to analyze the priority-based request scheduling mechanism (PRSM) behavior in the overloaded SIP server equipped with an infinite buffer. The PRSM has been proposed in our earlier studies to prevent the overloading of SIP servers, where the original SIP request messages have strict priority over the retransmissions. The performance evaluation of the proposed model for the PRSM and the reference model of the conventional SIP has been reported under various test scenarios. The experiment results of the proposed model verify the effectiveness of the PRSM fluid-flow model as we already demonstrated in our previous studies using our simulation tool. Without loss of generality, the numerical results closely match with the simulation results, indicating that the proposed models can accurately capture the dynamic behavior of a SIP server with the PRSM. [ABSTRACT FROM AUTHOR]

Abstract: Conventional blind audio watermark (WM) decoders use matched-filtering techniques because of their simplicity. In these methods, WM decoding and WM detection are often considered as separate problems and the WM signal embedded by spreading a secret key through the spectrum of a host signal is extracted by maximizing correlation between the secret key and the received audio. Conventionally decoding is achieved by using a pre-defined decoding/detection threshold and tradeoff between the false rejection ratio and false acceptance ratio constitutes main drawback of the conventional decoders. Unlike the conventional methods, this paper introduces a pattern recognition (PR) framework to WM extraction and integrates WM decoding and detection problems into a unique classification problem that eliminates thresholding. The proposed method models statistics of watermarked and original audio signals by a Gaussian mixture model (GMM) with K components. Learning of the embedded WM data is achieved in a principal component analysis (PCA) transformed wavelet space and a maximum likelihood (ML) classifier is designed for WM decoding. Robustness of the proposed method is evaluated under compression, additive noise and Stirmark benchmark attacks. It is shown that both WM decoding and detection performances of the introduced decoder outperform the conventional decoders. [Copyright &y& Elsevier]