Kernelized Correlation Filter (KCF) algorithm has been successfully applied in object tracking. By finding the maximum confidence in the search region, the candidate target block is determined by KCF with constant size. When the scale of the target changes during its actual movement, the traditional KCF algorithms often fail to track the target. Moreover, it is difficult to judge whether the object is missing due to the lack of self-adaptive threshold regulation scheme in the KCF tracking. In order to solve these problems, this paper proposes a scale-adaptive target tracking algorithm based on KCF, which is mainly divided into the following steps. Firstly, the positive and negative samples of the nearest neighbor classifier are initialized by the selected target and its surrounding non-target areas. Secondly, the peak point of the spectral response of the current frame image is obtained by executing the KCF algorithm, which is the target center point. Thirdly, the scale change of object is obtained by calculating the ratio of the bandwidth of spectral response peak centered candidate regions and the target in the previous frame. Fourthly, the scaled candidate target is confirmed by calculating the sample similarity between it and the Nearest Neighbor Classifier (NNC). Finally, the positive and negative samples of the nearest neighbor classifier are updated with the confirmed tracking target and non-target respectively. Extensive experiments have been carried on four test video sequences. The experimental results show that our proposed method achieves a higher success rate and accuracy with less running time compared with the state-of-the- art methods.
KEYWORDS: Detection and tracking algorithms, Remote sensing, Data modeling, Principal component analysis, Statistical modeling, Image compression, Cameras, Data storage, Surveillance, Video surveillance
Person re-identification (Re-ID) is an important technique towards the automatic search of a person’s presence in a surveillance video or security systems. Applying incremental learning techniques to accelerate the online training speed with ever-increasing data is desired and critical for Re-ID. As an incremental learning algorithm, Incremental Kernel Null Foley-Sammon Transform (IKNFST) method significantly reduces the computational complexity while holds the accuracy. However, with ever-increasing person samples within the same category, the corresponding growth of dimensions makes it difficult to update the online model. To address the issue, we propose to maintain constant update speed by constructing Reduce Set (RS) expansions during online updating. The key idea is to firstly extract new information brought by newly-added samples and integrate it with the existing model by Incremental Kernel Principal Component Analysis (IKPCA) scheme for further Reduce Set (RS) compression. And the compressed samples and the corresponding model are then input to Kernel Null Foley-Sammon Transform (KNFST) algorithm for generating an updated model. Extensive experiments have been carried on three public datasets, including Market-1501, DukeMTMCReID and CUHK03. The results show that our proposed method beats the state-of-the-art IKNFST by a big margin.
KEYWORDS: RGB color model, Color difference, Data modeling, Monte Carlo methods, Inspection, Detection and tracking algorithms, Performance modeling, Process modeling, Printing, CMYK color model
During printing quality inspection, the inspection of color error is an important content. However, the RGB color
space is device-dependent, usually RGB color captured from CCD camera must be transformed into CIELAB color
space, which is perceptually uniform and device-independent. To cope with the problem, a Markov chain Monte Carlo
(MCMC) based algorithms for the RGB to the CIELAB color space transformation is proposed in this paper. Firstly, the
modeling color targets and testing color targets is established, respectively used in modeling and performance testing
process. Secondly, we derive a Bayesian model for estimation the coefficients of a polynomial, which can be used to
describe the relation between RGB and CIELAB color space. Thirdly, a Markov chain is set up base on Gibbs sampling
algorithm (one of the MCMC algorithm) to estimate the coefficients of polynomial. Finally, the color difference of
testing color targets is computed for evaluating the performance of the proposed method. The experimental results
showed that the nonlinear polynomial regression based on MCMC algorithm is effective, whose performance is similar
to the least square approach and can accurately model the RGB to the CIELAB color space conversion and guarantee the
color error evaluation for printing quality inspection system.
In the procedure of printing progress epically for multicolor printing, better ink controlling is very necessary and important to obtain high printing quality products and presswork and it is very crucial to monitor the ink on-line in the printing procedure. Automatic printing inking-up has become one important research topic in the field of printing industrial production and automatic control. This paper presents a method for monitoring and controlling the printing ink in the press ink fountain automatically using one controlled system based on ultrasonic sensor. Firstly, physical properties and the installation location of the ultrasonic sensor is explained and analyzed and one monitoring system consists of ultrasonic emitter, ultrasonic acceptor, voltage-magnified circuit, detecting circuit and alarming circuit is designed; then an improved on-line monitoring method is put forward and a system which can give alarm to add the printing ink while the ink in the ink fountain reaches the minimum position is developed. Lastly, the quantity of ink in the ink fountain can be displayed on-line and the new system using on-line monitoring method can realize the automation of inking-up in printing process. Experimental results in our laboratory show that this on-line monitor method can improve the automatic degree of most presses in our country, which will bring great economical and commercial benefits to our country's printing industry.
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