REEDS: Relevance and enhanced entropy based Dempster Shafer approach for next word prediction using language model

Proctor net: An AI framework for suspicious activity detection in online proctored examinations

A new low-light robust face super-resolution (SR) model via morphological transformation-based locality-constrained representation (MTLcR) is proposed to improve the robustness of the current SR algorithms against low-light problems. The proposed model first tries to detect the amount of low-light effects present in the input images with the help of dictionary images of the same domain. Further, the same amount of effect is applied to low-resolution (LR) dictionary images with the purpose of minimizing the reconstruction error while representing the input image as a weighted linear combination of LR dictionary images. In addition to this, morphological transformation is introduced in the objective function to further improve the reconstruction weights. The simulation results on a publicly available data set and some real-world images suggest the better reconstruction capability of the proposed model under low-light scenario as compared to other state-of-the-art works. 

Low-light robust face super resolution via morphological transformation based locality-constrained representation

At present, the detection and tracking for video moving object have been used in many fields. Aimed at the limitation of the traditional adjacent frame difference method, this paper presented the three-frame difference method to detect objects. For moving object tracking, this paper proposed a method combining Kalman filter and Mean-Shift, and used the prediction function of Kalman filter to overcome the defect of Mean-Shift in selecting the initial position of the candidate object. Experimental results showed that the detection and tracking method proposed in this paper are simple, precise, and perform a good result.

Improved moving object detection and tracking method

This article presents the implementation of a tracking system for a moving target using a fixed camera. The objective of this work is the ability to detect a moving object and locate their positions. In picture processing, tracking moving objects in a known or unknown environment is commonly studied. It is based on invariance properties of objects of interest. The invariance can affect the geometry of the scene or the objects. The proposed approach is composed of several steps; the first is the extraction of points of interest in the current image. Then, these points will be tracked in the following image by using techniques for calculating the optical flow. After this step, the static points will be removed to focus on moving objects, That is to say, there is only the characteristic points belonging to moving objects. Now, to detect moving targets using images of the video, the background is first extracted from the successive images. In our approach, a method of the average values of every pixel has been developed for modeling background. The last step which stays before switching to tracking moving object is the segmentation which allows identifying every moving object. And by using the characteristic points in the previous steps.

/pdf/new-approach-for-detecting-and-tracking-a-moving-object-2h3mopaohb.pdf

New Approach for Detecting and Tracking a Moving Object

Low-resolution face recognition based on feature-mapping face hallucination

To solve the problem of tracking an acoustic source in noise and reverberation environment, a new method is proposed in pursuit of higher accuracy. First, this paper improves the unscented particle filter, which can add the latest measurement information to optimize the proposal distribution. Then, the likelihood function is constructed by calculating the microphone arrays’ output energy in the framework of the improved algorithm. Finally, the experiment results indicate that the proposed localization method can not only improves the accuracy of location estimation, but also can enhance the ability to resist noise and reverberation in the acoustic source localization system. DOI : http://dx.doi.org/10.11591/telkomnika.v12i5.4816

/pdf/acoustic-source-localization-based-on-iterative-unscented-5el6ombwll.pdf

Acoustic Source Localization Based on Iterative Unscented Particle Filter

Dempster-Shafer evidence theory is widely used in the fields of decision level information fusion. In order to overcome the problem of the counter-intuitive results encountered when using Dempster’s combination rule to combine the evidences which exist high conflict, a modified sequential weighted evidence combination is proposed. Firstly, the credibility of each evidence can obtained based on K-L distance, besides, the uncertainty of each evidence can obtained based on information entropy. Simultaneously, using the uncertainty of each evidence to improve the credibility of each evidence, then the weights of the bodies of evidence are obtained based on the improved credibility of each evidence, the weights generated are used to modify the bodies of evidence including the previous combination result, the previous evidence and the new arriving body of evidence at current step. Finally, according to the Dempster’s combination rule, the weighted average combination results can be obtained. In the experimental part, the improved method is used to fuse video multiple features in target tracking system and compared the results with the standard D-S theory. The simulation results show that the proposed method has better performance. DOI : http://dx.doi.org/10.11591/telkomnika.v12i1.3364

/pdf/multiple-feature-tracking-based-on-the-improved-dempster-4zzkpev0az.pdf

Multiple-feature Tracking Based on the Improved Dempster-Shafer Theory

Multiple features fusion based tracking is one of the most active research in tracking literature, In this paper, a novel adaptive fusion strategy is proposed for multiple features fusion, based on two common used fusion rules: product rule and weighted sum rule. This strategy employs particle filtering technique; product rule and weighted sum rule are unified into an adaptive framework through defined features distance. In practice, the new fusion strategy shows more robustness than product fusion and weighted sun rule.

Object Tracking Based on Multiple Features Adaptive Fusion

In order to solve the poor robustness problem due to partial occlusionof target, we propose an adaptive particle filter tracking method based on thefusion of the multiple features. First, we regard information fusion as aprocess of information loss, loss coefficient is defined based on this idea.Then the credibility of feature can be obtained by weighted distance and themean of particle filter, and we use the features’ credibility to reallocate theloss coefficient. An extensive number of comparative experiments show theproposed algorithm is more stable and robust than multiplicative fusion andadditive fusion tracking algorithms.

Object Tracking Method Based on a New Multi-Feature Fusion Strategy

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