Application Of Video Surveillance System Research Articles

Bioinspired Garra Rufa Optimization-Assisted Deep Learning Model for Object Classification on Pedestrian Walkways.

Object detection in pedestrian walkways is a crucial area of research that is widely used to improve the safety of pedestrians. It is not only challenging but also a tedious process to manually examine the labeling of abnormal actions, owing to its broad applications in video surveillance systems and the larger number of videos captured. Thus, an automatic surveillance system that identifies the anomalies has become indispensable for computer vision (CV) researcher workers. The recent advancements in deep learning (DL) algorithms have attracted wide attention for CV processes such as object detection and object classification based on supervised learning that requires labels. The current research study designs the bioinspired Garra rufa optimization-assisted deep learning model for object classification (BGRODL-OC) technique on pedestrian walkways. The objective of the BGRODL-OC technique is to recognize the presence of pedestrians and objects in the surveillance video. To achieve this goal, the BGRODL-OC technique primarily applies the GhostNet feature extractors to produce a set of feature vectors. In addition to this, the BGRODL-OC technique makes use of the GRO algorithm for hyperparameter tuning process. Finally, the object classification is performed via the attention-based long short-term memory (ALSTM) network. A wide range of experimental analysis was conducted to validate the superior performance of the BGRODL-OC technique. The experimental values established the superior performance of the BGRODL-OC algorithm over other existing approaches.

Application of video surveillance system in the prevention and control of the Ebola treatment center in Liberia

Objective To investigate the effect of application of video surveillance system for medical staff in the prevention and control of Ebola treatment center (ETC). Methods The high-definition camera installed in the polluted area 100 beds of 24 wards, semi polluted area ring corridor, wear off gowns and other areas without blind spot. Video data and information were collected. The experts of steering group and medical personnel were on duty of video monitoring 24 hours in the medical duty room. They monitored the situation of frontline staff in medical care, whether wearing personal protective equipment (PPE), logistics and transportation problems encountered in the work, and corrected and reminded. Results From December 2015 to January 2014, by checking 182 people on the first-line staff including doctors, nurses and nursing workers, 239 problems of 4 categories were found. The PPE problems was 147 cases (61.50%), while perform operation process issue 35 cases (14.64%), flow logistics distribution problems 31 cases (12.97%), the others 26 cases (10.88%). All the problems were given a timely reminder. There was no infections among medical team. Conclusion Video surveillance system could help medical staff to prevent and control infection, and reduce the risk of infection. Key words: Video monitoring system; Ebola virus; Treatment center; Medical staff

Moving Shadow Detection Based on Multi-feature Analysis and Gray-scale Morphological Filtering

For many applications in video surveillance systems, shadow detection is a very important step for enhancing the accuracy of moving object segmentation. This paper proposes a robust multi-feature approach which makes integrated use of intensity, chromacity and texture for shadow elimination. After motion detection, foreground pixels are evaluated by several weak classifiers in a coarse-to-fine manner. In each level, the outputs of the weak classifiers are accumulated and formed as an evidence map. Then shadows are eliminated using a gray-scale morphological filtering. Results on various real surveillance videos show the effectiveness and robustness of the algorithm.

Study on the Application of Video Surveillance System of Expressway Construction Based on 3G

Safety and quality supervision is an important part of expressway construction, the traditional regulation mode relies on artificial which exists objectivity and poor oversight, poor technical means and other issues, which brought a significant security risks to the project quality and safety. In this paper, based on the analysis of the existing video surveillance system, We explored the application of 3G technology in video surveillance system in the expressway construction. First of all, Composition and principle of the system are discussed in this paper. Secondly, the platform constitute of wireless video surveillance system is analyzed in detail, Finally, We summarized the application advantages of 3G technology in the expressway construction ,which is of great significance to enhance Expressway construction management.

Multiclass object classification for real-time video surveillance systems

Object classification in video is an important factor for improving the reliability of various automatic applications in video surveillance systems, as well as a fundamental feature for advanced applications, such as scene understanding. Despite extensive research, existing methods exhibit relatively moderate classification accuracy when tested on a large variety of real-world scenarios, or do not obey the real-time constraints of video surveillance systems. Moreover, their performance is further degraded in multi-class classification problems. We explore multi-class object classification for real-time video surveillance systems and propose an approach for classifying objects in both low and high resolution images (human height varies from a few to tens of pixels) in varied real-world scenarios. Firstly, we present several features that jointly leverage the distinction between various classes. Secondly, we provide a feature-selection procedure based on entropy gain, which screens out superfluous features. Experiments, using various classification techniques, were performed on a large and varied database consisting of ∼29,000 object instances extracted from 140 different real-world indoor and outdoor, near-field and far-field scenes having various camera viewpoints, which capture a large variety of object appearances under real-world environmental conditions. The insight raised from the experiments is threefold: the efficiency of our feature set in discriminating between classes, the performance improvement when using the feature selection method, and the high classification accuracy obtained on our real-time system on both DSP (TMS320C6415-6E3, 600 MHz) and PC (Quad Core Intel® Xeon® E5310, 2 × 4 MB Cache, 1.60 GHz, 1066 MHz) platforms.