Changes In Video Sequence Research Articles

Traffic Sign Detection Under Challenging Conditions: A Deeper Look into Performance Variations and Spectral Characteristics

Traffic signs are critical for maintaining the safety and efficiency of our roads. Therefore, we need to carefully assess the capabilities and limitations of automated traffic sign detection systems. Existing traffic sign datasets are limited in terms of type and severity of challenging conditions. Metadata corresponding to these conditions are unavailable and it is not possible to investigate the effect of a single factor because of simultaneous changes in numerous conditions. To overcome the shortcomings in existing datasets, we introduced the CURE-TSD-Real dataset, which is based on simulated challenging conditions that correspond to adversaries that can occur in real-world environments and systems. We test the performance of two benchmark algorithms and show that severe conditions can result in an average performance degradation of 29% in precision and 68% in recall. We investigate the effect of challenging conditions through spectral analysis and show that challenging conditions can lead to distinct magnitude spectrum characteristics. Moreover, we show that mean magnitude spectrum of changes in video sequences under challenging conditions can be an indicator of detection performance. CURE-TSD-Real dataset is available online at https://github.com/olivesgatech/CURE-TSD.

ANTIC: antithetic isomeric cluster patterns for medical image retrieval and change detection

In this study, new feature descriptors are designed for medical image retrieval and change detection applications, respectively. Inspired by isomerism, the authors propose a novel feature descriptor named antithetic isomeric cluster pattern (ANTIC). The ANTIC is defined by the two properties: cluster patterns and antithetic isomerism (ANTI). The cluster pattern corresponds to successive pixel intensity differences at antithetical orientations. Furthermore, the ANTI is characterised by two aspects: first, the clusters are oppositely oriented (antithetical) to each other and second, both adhere to a defined isomeric property. The ANTIC identifies the lines and corner point information in the local neighbourhood across various directions. To attain enhanced robustness, they further proposed multiresolution ANTIC by integrating the multiresolution Gaussian filter. Moreover, to reduce the feature dimensionality, they extended their work to rotation invariant features. The proposed method outperforms other widely used feature descriptors in biomedical and retinopathy image retrieval applications. In addition, they extracted spatiotemporal features by designing intra‐ANTIC and inter‐ANTIC to detect motion changes in video sequences. They validated the effectiveness of these features by conducting experiments on CDNet 2014 dataset. The proposed descriptor achieves better performance in various challenging conditions for change detection as compared to other state‐of‐the‐art techniques.

An efficient estimation method for intensity factor of illumination changes

For intensity correction under illumination changes in video sequence, one of the main problems lies in the lack of adaptive technique for the classification of stationary and non-stationary pixels in the images. In this paper, we propose an efficient estimation approach for intensity factor of illumination changes in order to perform intensity correction for dynamic sequence images. Firstly, the ratio image is obtained, where the probability density function computed on pixel values can be considered as a mixture Gaussian model. Then the process of the parameter estimations is performed based on Expectation---maximization (EM) algorithm. Under the assumption of Gaussian distribution for the values of stationary pixels, the intensity factor can be estimated by using pixels adjacent to the mean value of Gaussian distribution related to the stationary class. Finally, two experiments are carried out to verify the proposed method.

Automatic Facial Expression Recognition by Facial Parts Location with Boosted-LBP

This paper proposes an automatic facial expression recognition system, which uses new methods in both face detection and feature extraction. In this system, considering that facial expressions are related to a small set of muscles and limited ranges of motions, the facial expressions are recognized by these changes in video sequences. First, the differences between neutral and emotional states are detected. Faces can be automatically located from changing facial organs. Then, LBP features are applied and AdaBoost is used to find the most important features for each expression on essential facial parts. At last, SVM with polynomial kernel is used to classify expressions. The method is evaluated on JAFFE and MMI databases. The performances are better than other automatic or manual annotated systems.

A real-time stereo depth extraction hardware for intelligent home assistant robot

One of the fastest growing next generation consumer products is a robot technology industry and it is rapidly forming a huge market. A depth extraction is one of the key techniques to adopt a home assistant robot for avoiding obstacles and looking for a transfer route. However, a home assistant robot mounted stereo vision systems produce unpredictable changes in video sequences when the robot is walking and needs hard computation to produce an accurate estimation. In this paper, we propose hardware based realtime stereo depth extraction method for an intelligent home assistant robot. For fast adaption of the external environment, we present depth map extraction algorithm by using preprocessing, parallel prediction searching employing median filter. Experimental results show that the proposed method reduces the processing time and the energy consumption compared to the conventional methods. This implementation is suitable for adaptive real-time depth extraction that is compatible with current robot applications.

Online subspace learning using a misalignment detection for tracking a talking face in video sequence

Facial appearance changes in video sequences represent a nonstationary data problem, because of factors such as variation in pose, illumination and facial expressions. While most algorithm, that employ fixed appearance models of the target object, are not robust to track objects in uncontrolled environments. Existing Adaptive Appearance Models ( AAMs) approaches solve this problem to an extent. However, they do not adequately track facial feature points, such as those relating to the eyes or mouth in the presence of significant expression changes. In this paper, we propose a method to combine an online and an offline learning for robust tracking of facial feature points. Our method firstly estimates facial feature points globally with a stochastic approach which allows to escape from local minimum. We then refine the feature points with a deterministic approach. The tracked results are filtered by offline learning approach to ensure rejection of poorly aligned targets. This allows the proposed tracker to significantly improves robustness against appearance changes and occlusions. Experiment results on tracking facial feature points in long video sequences with a wide range of facial expressions in head movement demonstrate the effectiveness and robustness of our tracker.

Discussion of An Analysis of the Group Dynamics Surrounding Internal Control Assessment in Information Systems Audit and Assurance Domains

This paper presents a method for detecting scene changes in video sequences, in which the -test is modified by imposing weights according to NTSC standard. To automatically determine threshold values for scene change detection, the proposed method utilizes the frame differences that are obtained by the weighted -test. In the first step, the mean and the standard deviation of the difference values are calculated, and then, we subtract the mean difference value from each difference value. In the next step, the same process is performed on the remained difference values, mean-subtracted frame differences, until the stopping criterion is satisfied. Finally, the threshold value for scene change detection is determined by the proposed automatic threshold estimation algorithm. The proposed method is tested on various video sources and, in the experimental results, it is shown that the proposed method is reliably estimates the thresholds and detects scene changes.