Root 권한 프로세스 추적을 통한 침입 탐지 기법

This paper presents a robust real-time method for general detection of abandoned and removed objects from surveillance videos. The system introduces a unique combination of a new pixel-wise static region detector and a novel abandoned/removed object classifier based on color richness. In the static region detection phase, two backgrounds are constructed respectively to build foreground and stationary masks which are then used to update a static region confidence map. Static regions are thus extracted from the confidence map and further classified into abandoned or removed items by comparing color richness between the background and current frame. Our algorithm is easy to implement, robust to small repetitive motions, illumination change and can handle object occlusion. Experimental results on two public video databases which are shot in different scenarios demonstrate the robustness and practicability of the proposed method in real-time video surveillance.

Facial expression scrutiny has attracted tremendous consciousness in the area of computer vision because it plays a prime role in the domain of human-machine communication.Smiley face expressions are generated by slimming down of facial muscles, which results in temporally buckled facial features such as eye lids, eye brows, nose, lips, and skin texture.These are evaluated by three characteristics: those portions of the face that will take part for facial action, the intensity of facial actions, and the dynamics of facial actions.In this paper we propose a real-time, accurate, and robust smile detection method based on genetic algorithm.We generated leaf-matrix to extract target expression.Finally, we have compared our methodology with the smile shutter function of Canon Camera.We have achieved better performance than Sony on slight smile.

Lane detection is an essential visual capability for the autonomous vehicle navigation used in the intelligent transportation system (ITS). Several approaches for lane detection have been suggested in the past. However, there is still one issue about robustness. This paper presents a robust and real-time multi-lane detection method based on road marking feature points (RMFP) for autonomous vehicle navigation in an urban environment. The key idea in this paper is to apply methods from extracting RMFP and the target tracking domain to identify lane information. Then the RMFP is extracted from the gray-scale image and the IPM image. Additionally, the lane line color and structure features are used to sift through the RMFP that meet lane lines. Lastly, the clustering method is used to generate lane lines, and these lines are tracked by frame association and Kalman Filter. The experiment results based on image sequences taken under various visual conditions showed that the proposed system can identify the lane line markings with 98% accuracy. The method shows much robustness under various complicated scenarios and meets the real-time requirement.

A robust and real-time lane detection method in low-light scenarios to advanced driver assistance systems

A method of real-time object detection using Radial Reach Correlation (RRC) has been developed. We also apply a statistical background estimation to cope with dynamic and complex environments. In order to reduce the computational cost of RRC for an embedded image processing device, we apply two techniques: fast RRC algorithm and background estimation based on statistical approach with cumulative averaging process. As a result, it is possible to reduce the RRC calculation process to about 1/4 in comparison with normal RRC. By combining these techniques, we can achieve robust real-time object detection.

A new image-based real-time flame detection method is proposed in this paper. First, fire flame features based on the HSI color model are extracted by analyzing 70 flame images. Then, based on these flame features, regions with fire-like colors are roughly separated from an image. Besides segmenting fire flame regions, background objects with similar fire colors or caused by color shift resulted from the reflection of fire flames are also separated from the image. In order to get rid of these spurious fire-like regions, the image difference method and the invented color masking technique are applied. Finally, a simple method is devised to estimate the burning degree of fire flames so that users could be informed with a proper warning alarm. The proposed method is tested with seven diverse fire flame video clips on a Pentium II 350 processor with 128 MB RAM at the process speed of thirty frames per second. The experimental results are quite encouraging. The proposed method can achieve more than 96.97% detection rate on average. In addition, the system can correctly recognize fire flames within one second on the initial combustion from the test video clips, which seems very promising.

This paper addresses the problem of segmenting audio data recorded with embedded devices for the purpose of intelligent sensing in the context of multi-modal interactions. We propose a real-time method for robust speech detection in natural, noisy environments. It is based on a fusion of high order statistics of the LPC residual and autocorrelation, and adopts an on-line version of Expectation Maximization algorithm for the classification. Experimental evaluations show that the proposed method provides better detection performance under different types of natural noises, working robustly against other voices in the context of multi-speaker interactive situations. As the proposed method is based on features which have a low computational cost, and has a small latency, it is suitable for real-time tracking applications.

For the lack of current extraction methods of traffic basic data, a traffic information acquisition method based on improved Kalman filtering using traffic video was presented in this paper. The Gaussian mixture model was improved for multi-vehicle moving targets detection. In order to further improve the detection efficiency, a heuristic improvement method was proposed. For the matching problem of multiple targets in the continuous video frame, combining the vehicle movement characteristic, the Kalman filter was used to estimate the vehicle position optimally, a real-time traffic detection method of matching the target chain was proposed. Finally, the experiment was carried out with the actual transportation video, results show that the proposed method can effectively improve the noise interference and foreground blurring in Multi-target vehicle detection, and can extract the vehicle moving target information from different traffic environments with high accuracy, different models and vehicle color The lowest detection rate was 93.08%.

The robustness and stability of lane detection is vital for advanced driver assistance vehicle technology and even autonomous driving technology. To meet the challenges of real-time lane detection in complex traffic scenes, a simple but robust multilane detection method is proposed in this paper. The proposed method breaks down the lane detection task into two stages, that is, lane line detection algorithm based on instance segmentation and lane modeling algorithm based on adaptive perspective transform. Firstly, the lane line detection algorithm based on instance segmentation is decomposed into two tasks, and a multitask network based on MobileNet is designed. This algorithm includes two parts: lane line semantic segmentation branch and lane line Id embedding branch. The lane line semantic segmentation branch is mainly used to obtain the segmentation results of lane pixels and reconstruct the lane line binary image. The lane line Id embedding branch mainly determines which pixels belong to the same lane line, thereby classifying different lane lines into different categories and then clustering these different categories. Secondly, the adaptive perspective transformation model is adopted. In this model, the motion information is used to accurately convert the original image into a bird’s-eye view image, and then the least-squares second-order polynomial fitting is performed on the lane line pixels. Finally, experiments on the CULane dataset show that the proposed method achieved similar or better performance compared with several state-of-the-art methods, the F1 score of the proposed method in the normal test set and most challenge test sets is better than other algorithms, which verifies the effectiveness of the proposed method, and then the field experiments results show that the proposed method has good practical application value in various complex traffic scenes.

In this paper, we consider water surface object detection in natural scenes. Generally, background subtraction and image segmentation are the classical object detection methods. The former is highly susceptible to variable scenes, so its accuracy will be greatly reduced when detecting water surface objects due to the changing of the sunlight and waves. The latter is more sensitive to the selection of object features, which will lead to poor generalization as a result, so it cannot be applied widely. Consequently, methods based on deep learning have recently been proposed. The River Chief System has been implemented in China recently, and one of the important requirements is to detect and deal with the water surface floats in a timely fashion. In response to this case, we propose a real-time water surface object detection method in this paper which is based on the Faster R-CNN. The proposed network model includes two modules and integrates low-level features with high-level features to improve detection accuracy. Moreover, we propose to set the different scales and aspect ratios of anchors by analyzing the distribution of object scales in our dataset, so our method has good robustness and high detection accuracy for multi-scale objects in complex natural scenes. We utilized the proposed method to detect the floats on the water surface via a three-day video surveillance stream of the North Canal in Beijing, and validated its performance. The experiments show that the mean average precision (MAP) of the proposed method was 83.7%, and the detection speed was 13 frames per second. Therefore, our method can be applied in complex natural scenes and mostly meets the requirements of accuracy and speed of water surface object detection online.

In order to handle a computer security incident or network failure, it is important to grasp a list of pairs of IP and MAC addresses of hosts. Ones may then traditionally poll an Address Resolution Protocol (ARP) table of a core switch at an interval using SNMP or other methods. This traditional method based upon polling, however, has two major drawbacks that (1) some pairs of IP and MAC addresses may not be obtained and (2) incurs a heavy load on a core switch. This paper then proposes AXARPS that is the novel scalable ARP snooping to build a list of pairs of IP and MAC addresses. AXARPS can avoid missing pairs of IP and MAC addresses by monitoring all ARP traffic instead of the traditional method. AXARPS also can reduce a CPU load on a recent high-end core switch by approximately 20% in comparison with the traditional method. AXARPS is scalable because AXARPS incurs no additional CPU load even the number of hosts increases. AXARPS employs a policy-based mirroring of a switch that mirror traffic that matches a specified filter. The policy-based mirroring can then mirror ARP traffic only, and reduce a load on an ARP parsing server.

Robust real-time hand detection and localization for space human–robot interaction based on deep learning

As one of the most common structural forms in port engineering, the operation environment of high-pile wharf is quite harsh and complex, and its pile foundation often produces structural damage of different degrees. Until now, there is a lack of efficient, safe and economic damage detection methods. A novel and precise real-time structural damage detection (SDD) method using both finite element modelling (FEM) and 1D convolutional neural networks (CNNs) is established in this study. The results indicate that the proposed method could accurately identify the presence and location of damage in real time. The results also demonstrated that the proposed 1D CNNs based model are more sensitive to the longitudinal and lateral displacement responses of the high-pile wharf structure.

The aim of this paper is to present a fast, robust and adaptive method for real-time 3D hand detection. Unlike traditional 2D approaches which are based on skin or features detection, the proposed method relies on depth information obtained from a stereoscopic video system. As a result, it provides the 3D position of both hands. It also deals with changes of lighting and it is capable of detecting the hands from long distances. The experimental results showed that our method can be successfully integrated in various and complex vision-based systems requiring real-time recognition of hands gestures in 3D environments.

An improved tiny-yolov3 pedestrian detection algorithm