Video Surveillance Cameras Research Articles

Effect of drinking water temperature and mineral supplementation on pre and post-lambing behavior of Corriedale ewes and their newborn lambs.

Lamb mortality poses a serious challenge to the sheep industry, leading to immediate financial losses for livestock owners. Improvement of management practices, particularly through environmental enrichment, has been consistently linked to increased lamb survival and growth in both ewes and lambs due to its substantial impact on their behavior and overall welfare. In our study, we aimed to enhance ewe production and improve ewe behavior through the optimization of water temperature and the inclusion of mineral supplementation. To conduct our research, four distinct groups comprising 10 ewes each were formed based on body weight and parity. Our comprehensive study involved the observation of both pre and post-lambing behaviors in ewes, as well as the examination of lamb behavior. The pregnant ewes were monitored using video camera surveillance. For studying the pre-partum behavior, videos were thoroughly analyzed for 3h before parturition. For studying post-partum and lambing behavior, videos were analyzed for 3 days (24h per day). Notably, the group that received warm water and mineral supplementation exhibited a discernibly positive behavioral response. These findings illuminate a promising approach for improving ewe production while simultaneously reducing lamb mortality. Our research contributes valuable insights into the sheep industry, offering a pathway toward enhanced animal welfare, economic sustainability, and increased profitability for livestock owners.

Principles of suspicious activity detection and programs for analyzing suspicious activity using video surveillance cameras

This article delves into the principles of detecting suspicious activity, meticulously examining both logical and psychological facets of human behavior. It places a primary focus on diverse programs for analyzing suspicious activity using video surveillance cameras. A comprehensive overview is provided for key categories of suspicious activity, encompassing crowd clustering, rapid movement, intrusion into secure areas, and unattended items. Within the research framework, the advantages and drawbacks of leading programs for suspicious activity analysis through video surveillance are scrutinized. Furthermore, the article introduces the concept of an innovative program aimed at competing with existing solutions. This software aspires to amalgamate the strengths of competitors while avoiding their shortcomings. The research goal is to identify the potential for creating a highly effective software product capable of successfully competing and meeting the requirements of modern surveillance systems.

Stand-off concealed firearm detection using motion tracking and convolutional neural networks

<p>The standoff detection of concealed firearms is crutial in managing public security in public spaces. Currently employed standoff concealed weapon detection techniques employ electromagnetic wave imaging which has been found to be extremely slow and may require expensive hardware and may not be applicable in public open spaces. Inorder to maintain safety in open spaces, artificial intelligence enabled video surveillance systems have been widely adopted. This poses an opportunity to explore video surveillance cameras as concealed weapon detectors. A review of existing video surveillance based automated weapon detection approaches discovered that the focus was on the detection of unconcealed firearms leaving a gap in the detection of concealed firearms. This study addresses the aforementioned gap by providing a standoff concealed firearm detection approach on video based on skeletal-based human motion tracking and convolutional neural networks. The motion of armed and unarmed persons was tracked using a depth camera and further classified using convolutional neural networks model. The developed model reported 100% accuracy, precision and recall scores. These results outperformed results obtained from traditional machine learning models therefore highlighting the superior capability of the proposed approach for concealed firearm detection on video to complement the efforts of human video surveillance operators.</p>

Choice of architecture of video surveillance systems of State Border Guard of Ukraine based on cloud and fog technologies

Modern video surveillance systems have evolved from closed-circuit television systems to the level of complex systems that operate as part of complex integrated systems and solve the tasks of not only recording events in the sectors of view of video surveillance cameras but also analysing the received video information. The increase in the amount of information circulating in modern systems requires the creation of new concepts. In recent years, the attention of scientists has been drawn to distributed information processing technologies. The concept of cloud computing is developing rapidly, and the basic ideas that were laid down in its construction can no longer solve the problems faced by the technology. This has led to the emergence of so-called post-cloud architectures that extend and complement the capabilities of cloud computing. These architectures include Mist, Edge, Fog, etc. The purpose of the article is to study video surveillance systems based on the concept of cloud and fog computing and criteria for assessing their effectiveness. The research was conducted using the methods of analysis and generalisation, modelling, and experimentation. This approach made it possible to conduct a comparative study of video surveillance systems built on two architectures. The results obtained indicate that fog computing technology has the advantage of reducing latency, minimising the need for repeated requests to the cloud by performing calculations at the cloud edge. In a model based on the cloud computing concept, services that use cloud resources lead to an increase in network load. At the same time, fog computing technology allows you to relieve the network load by performing part of the computation by fog nodes. The results of the experimental study show the advantages of fog computing for networks that are sensitive to delays. However, if we consider a video surveillance system with the main task of recording events in the sectors of view of CCTV cameras and the ability to view video information in real time, it is obvious that fog nodes will not provide long-term storage of video information, and delays will not be critical. In the case of a video surveillance system with video analytics functions, fog nodes will be able to perform part of the video analytics algorithms, thus unloading the cloud. Therefore, an urgent task is to study the effectiveness of building video surveillance systems with video analytics functions based on fog architecture.

Consolation in the aftermath of violent public assaults: An interaction ethological study

AbstractAnimal ethologists suggest that non-human primates console victims of aggression in a manner similar to humans. However, the empirical basis for this cross-species comparison is fragile, given that few studies have examined consolation behavior among humans. To address this gap, we revive and apply the underappreciated ethological branch of micro-sociology, which advocates the study of human interactions by applying ethological observation techniques. We thus systematically observed naturally occurring human consolation captured by video surveillance cameras in the aftermath of violent public assaults. Consistent with prior human and non-human primate research, social affiliation promoted consolatory helping. By contrast, we found no main effect of sex. A further exploratory analysis indicated an interaction effect between social affiliation and sex, with female affiliates having the largest probability of providing consolation. We discuss implications for the cross-species study of primate consolation and advocate that micro-sociology should reappraise ethological perspectives.

Adaptive Processing of Camera Video Stream with Limitations on the Network Data Transmission Bandwidth

Video surveillance systems, cameras, and video stream processing are actively used in many enterprises as a monitoring and control tool for regular and emergency situations, as well as staff activities. The application of intelligent algorithms allows tracking and minimizing operator errors, but these approaches are highly sensitive to the quality of the original video, presence of noise, and low resolution. On the other hand, such video surveillance systems may be limited by network bandwidth. Therefore, this work considers an adaptive video stream processing algorithm that ensures efficient operation of computer vision and object recognition methods while minimizing the amount of transmitted information within network bandwidth constraints. The proposed algorithm addresses the task of determining boundary conditions that ensure the functionality of object recognition algorithms with the least amount of video stream. Corresponding experimental studies were conducted to determine the minimum values of frame resolution and video bitrate. The algorithm was tested in organizing video surveillance at warehouse complexes. The obtained results can be used in developing decision support systems for enterprises in various industries requiring intelligent processing of large volumes of data.

Real Time Weapon Detection using YOLOv8 and Alert Mechanism

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention.

Real Time Weapon Detection using YOLOv8 and Alert Mechanism

Abstract: Security cameras and video surveillance cameras have become an important partof public safety. However, in many cities, thesesystems still manually detect high-risk situations. Understaffing in security services can lead to delays in detecting incidents or unforeseen threats, putting the public at risk. The aim of this project is to develop a low-cost, effective intelligencebased solution for real-time weapons detection and surveillance video analysis in different situations. As can be seen from many statistics, the incidence of gun and dangerous weapon crimes is increasing every year, making it difficult for the police to solve the problem in time. Crimes caused by guns or knives are very common in many places, especially in places where gun laws do not exist. Early detection of crime is critical to public safety. One way to prevent these situations is to use video surveillance to detect the presence of dangerous weapons such as guns and knives. Monitoring and control now also require monitoring and intervention. Here, we present in video a system for tracking weapons suitable for tracking and controlling targets. We use the YOLOv8 (look once) algorithm to detect weapons in live video. YOLO model is an end-to-end deep learning model; it is very popular because it is fast and accurate. Previous methods such as region-based convolutional neural networks (R-CNN) required thousands of network tests to make predictions for an image, which could be time-consuming-Optimization is a laborious and painful process. He focuses on specific areas ofpainting and trains everything personally. The YOLO model, on the other hand, passes the image through the neural network only once. Since speed isimportant in real-time video, we use the YOLOv8 algorithm. This data was trained to classify three groups of weapons (pistols, knives, and artillery). When a weapon is detected, an alert is sent to authorities who can take action and reduce crime before it becomes a crime. Powered by Tensorflow, the system took 294 seconds in the first test toidentify weapon types in 9 categories.

Analysis of activity rhythm and behavior pattern for plateau pika in degraded alpine meadow

This paper proposes a behavior observation method for plateau pika (Ochotona curzoniae) in degraded alpine meadow in the Source Zone of the Yellow River (SZYR) on the Qinghai–Tibet Plateau based on continuous monitoring videos to observe its activity rhythm. The video surveillance cameras are installed facing toward degraded alpine meadow in the SZYR and run from March 2021 to February 2022 to record time allocation of plateau pika behavior (i.e., digging, foraging, guarding, moving and other behaviors) and its daily, monthly, and seasonal activity rhythm. The results indicate that: (1) The most active ground activity time is between 10:00 and 16:00, meanwhile it is inactive at night, which proves it plateau pika is diurnal animals. (2) Digging and foraging are frequently occurring behaviors among its daily activities, which occurs from 9:00 to 12:00 and 16:00. (3) Compare to other season, winter sees relatively frequent occurrences of digging and foraging according to the seasonal analysis results. (4) From annual perspective, digging and foraging accounted for 66.13% of plateau pika activity, while guarding accounted for 13.35%, moving accounted for 11.64%, and other behaviors accounted for 8.88%. There is a significant difference in the frequency of each behavior between the two groups (P < 0.05). The behavior observation method using video surveillance camera contributes to behavioral ecology information of plateau pika.

Docker Container-Based Framework of Apache Kafka Node Ecosystem: Vehicle Tracking System by License Plate Recognition on Surveillance Camera Feeds

Vehicle tracking and license plate recognition (LPR) over video surveillance cameras are essential intelligent traffic monitoring systems. Due to the enormous amount of data collected each day, it would be difficult to track vehicles by license plate in a real-world traffic setting. Large volumes of data processing, real-time request responses, and emergency scenario response may not be possible using conventional approaches. By combining license plate recognition with the docker container-based structure of the Apache Kafka node ecosystem, the suggested solution takes a novel approach to vehicle tracking. The primary components of our suggested framework for reading license plates are the identification of license plates and text data queries. License plate localization is performed with You Only Look Once version 3 (YOLOv3) and character recognition with Optical Character Recognition (OCR). The detected vehicle images with license plate results are published on related topics with Apache Kafka. Apache Kafka is a publish-subscribe (producers-consumers) messaging system and one of the most popular architectures used for streaming data. For each license plate search, a topic will be created in the framework where producers publish and consumers receive data. Thus, the workload of the operators will be reduced and they will be able to pay attention to more important events in traffic.

Tracking illegal activities using video surveillance systems: a review of the current state of research

The current state of research on the use of the neural networks under martial law to identify offenders committing illegal acts, prevent acts of terrorism, combat sabotage groups in cities, track weapons and control traffic is considered. The methods of detecting illegal actions, weapons, face recognition and traffic violations using video surveillance cameras are analysed. It is proposed to introduce the studied methods into the work of “smart” video surveillance systems in Ukrainian settlements.&#x0D; The most effective means of reducing the number of offences is the inevitability of legal liability for offences, so many efforts in law enforcement are aimed at preventing offences. Along with public order policing by patrol police, video surveillance is an effective way to prevent illegal activities in society. Increasing the coverage area of cameras and their number helps to ensure public safety in the area where they are used. However, an increase in the number of cameras creates another problem which is the large amount of video data that needs to be processed. To solve the problem of video data processing, various methods are used, the most modern of which is the use of artificial intelligence to filter a large amount of data from video cameras and the application of various video processing algorithms. The ability to simultaneously process video data from many CCTV cameras without human intervention not only contributes to public safety, but also improves the work of patrol police. The introduction of smart video surveillance systems allows monitoring the situation in public places around the clock, even if there is no police presence in the area.&#x0D; In the reviewed studies of video surveillance systems, neural networks, in particular MobileNet V2, YOLO, mYOLOv4-tiny, are used to track illegal actions, criminals and weapons, which are trained on large amounts of video and photo data. It has been found that although neural networks used to require a lot of computing power, they can now be used in IoT systems and smartphones, and this contributes to the fact that more video surveillance devices can be used to monitor the situation.

ВИЯВЛЕННЯ ОБ’ЄКТІВ НА ЗОБРАЖЕННІ В ПОТОКОВОМУ РЕЖИМІ ПРИ ВИКОРИСТАННІ YOLOv5 і FASTER R-CNN

The accuracy of the model can be one of the main indicators, on a basis of which it is possible to conclude about the suitability of the model for its practical operation. However, taking into account the specifics of the identified task, it is also worth paying attention to the speed of the model, since there is a need to process data in streaming mode. To investigate the possibilities of using machine learning in an applied problem, two groups of object recognition models considered: YOLOv5 and Faster R-CNN. The purpose of the study is to analyze the architectural solutions of the most common object detection models YOLOv5 and Faster R-CNN to build a model to improve the speed and accuracy of object detection in an applied task or further combine them. A total of 550 training images and 105 validation images collected. A dataset of 573 images from the new location also collected for final validation of the models. The use of Roboflow provided for image annotation, which allows not only to mark images, but also to export annotated data sets in various formats. Training and validation of the models carried out on the Google Colab platform. The platform uses the Python programming language and the PyTorch framework. The yolov5 and detecron2 libraries for YOLOv5 and Faster R-CNN, respectively, used for model training and validation. To determine whether the result belongs to one of the four groups, the IOU metric is used, which is the ratio of the intersection area to the area of the union of the correct and predicted bounding frames. The size of the trained YOLOv5 and Faster R-CNN models was 40.2 MB and 230.8 MB, respectively. The models tested on the second validation set. As result of the study, a set of data from video surveillance cameras collected and anno-tated using RoboFlow. The main representatives of two groups of object detection algorithms YOLOv5 and Faster R-CNN trained using the prepared data set. The results showed that both models have their advantages and disadvantages, both models are applicable for different tasks.

Efficient Human Violence Recognition for Surveillance in Real Time.

Human violence recognition is an area of great interest in the scientific community due to its broad spectrum of applications, especially in video surveillance systems, because detecting violence in real time can prevent criminal acts and save lives. The majority of existing proposals and studies focus on result precision, neglecting efficiency and practical implementations. Thus, in this work, we propose a model that is effective and efficient in recognizing human violence in real time. The proposed model consists of three modules: the Spatial Motion Extractor (SME) module, which extracts regions of interest from a frame; the Short Temporal Extractor (STE) module, which extracts temporal characteristics of rapid movements; and the Global Temporal Extractor (GTE) module, which is responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated for its efficiency, effectiveness, and ability to operate in real time. The results obtained on the Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to various alternatives. In addition, the VioPeru dataset was created, which contains violent and non-violent videos captured by real video surveillance cameras in Peru, to validate the real-time applicability of the model. When tested on this dataset, the effectiveness of our model was superior to the best existing models.

Using Neural Networks to Identify Objects in an Image

A modified neural network model based on Yolo V5 was developed and the quality metrics of object classification on video images built on the basis of existing known basic neural network architectures were compared. The application of convolutional neural networks for processing images from video surveillance cameras is considered in order to develop an optimized algorithm for detecting and classifying objects on video images. The existing models and architectures of neural networks for image analysis were analyzed and compared. The possibilities of optimizing the process of image analysis using neural networks are considered.

СПЕЦИФИКА ДИАГНОСТИКИ СИСТЕМЫ ВПРЫСКА ДВС В УСЛОВИЯХ ОГРАНИЧЕННЫХ ТЕХНИЧЕСКИХ ВОЗМОЖНОСТЕЙ ОСНАЩЕНИЯ СТАНЦИИ ТЕХНИЧЕСКОГО ОБСЛУЖИВАНИЯ

The article is devoted to the problems of diagnostics of gasoline engines of passenger cars of domestic and foreign production, regardless of their brands and models with an injection system, under limited conditions, the technical possibility of equipping the engine diagnostic station with the necessary instruments and equipment, as well as qualified specialists. The number of growing cars in one year in Russia is approximately 2.3%, while the model range is updated by 1%. The functionality of car electronics due to the introduction and implementation (pulse width modulators, without wired data transmission systems, increased sensors and video surveillance cameras, alarms and can bus) increases by 10%, thereby outstripping the production and technical base of car service stations. The use and implementation of the can bus (high-speed data transmission) allows electronic control units of modern cars to help the driver in non-standard situations on highways and monitor the driver's actions in emergency cases in order to avoid a traffic accident, in addition, the electronic system allows you to execute voice commands of the driver or passengers, thereby eliminating mechanical impact on the control buttons of that or another function. Given the complexity and modernity of diagnostic equipment, appropriate personnel who have received qualified training in the region under consideration are also needed. The problems in the diagnosis of new cars with extensive electronic systems capabilities and the non-compliance of most service stations and diagnostic points of the passenger car engine injection system in a particular region of the Republic of Dagestan with the level of development and compliance of the production and technical base meeting the requirements for qualified repair of modern cars of domestic and foreign production, for objective reasons, have been studied and investigated. Effective methods of equipping the diagnostic post with various equipment with minimal loss of occupied space are recommended.

Prospects of application of modern technologies of remote sensing of the earth in the forest industry

Currently, forest enterprises are characterized by depletion of forest resources and their significant remoteness. To solve a whole range of tasks related to forest management, monitoring of the environmental situation, control of the forest fire situation, remote sensing systems of the Earth have been developed and, to date, are successfully used. At the moment, such systems are located in almost every region, allowing dispatch centers to quickly perform their work. On the basis of the centers there are several operators who transmit information to the forestry and monitor a lot of video surveillance cameras in real time. Cameras help to detect fires and various smoke points much faster and more efficiently than satellite photography. However, the existing methods for detecting fires in forests have long been outdated and require the introduction of modern auxiliary engineering software solutions in this process. Research and development of systems that allow tracking fires that may be located far from human habitats, and their implementation in the work of forest protection enterprises is a top priority in the Ministries of Forestry.

R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video.

The security of civilians and high-profile officials is of the utmost importance and is often challenging during continuous surveillance carried out by security professionals. Humans have limitations like attention span, distraction, and memory of events which are vulnerabilities of any security system. An automated model that can perform intelligent real-time weapon detection is essential to ensure that such vulnerabilities are prevented from creeping into the system. This will continuously monitor the specified area and alert the security personnel in case of security breaches like the presence of unauthorized armed people. The objective of the proposed system is to detect the presence of a weapon, identify the type of weapon, and capture the image of the attackers which will be useful for further investigation. A custom weapons dataset has been constructed, consisting of five different weapons, such as an axe, knife, pistol, rifle, and sword. Using this dataset, the proposed system is employed and compared with the faster Region Based Convolution Neural Network (R-CNN) and YOLOv4. The YOLOv4 model provided a 96.04% mAP score and frames per second (FPS) of 19 on GPU (GEFORCE MX250) with an average accuracy of 73%. The R-CNN model provided an average accuracy of 71%. The result of the proposed system shows that the YOLOv4 model achieves a higher mAP score on GPU (GEFORCE MX250) for weapon detection in surveillance video cameras.

Using an Artificial Neural Network to Record and Analyse the Performance Indicators of a Transport Enterprise

Recently, the issue of determining actual values of passenger flow processed by urban public and suburban transport on each section of the route in real time has become even more relevant since those values affect planning, organisation of operations and performance of transport enterprises.It is not possible to assess the real volume of passenger flow with the number of tickets issued to passengers due to the large number of unaccounted passengers (the discrepancy between the number of tickets issued to passengers and the real number of passengers transported).The objective of the study which results are described in the article was to develop an intelligent system for calculating and analysing the transport enterprise performance, which allows automatically collecting, processing and analysing information about passenger flows, calculating the passenger rotation ratio on the route, drawing up optimal bus schedules and timetables, adjusting vehicle traffic intervals, determining the need for rolling stock to minimise the likelihood that a passenger is denied boarding, improving the quality of passenger service and reducing the operating costs of a transport enterprise.Real-time calculation of passenger flow values for each vehicle on the route of urban public transport is carried out by a quantitative method based on artificial neural network technology, which allows automatic processing of a large amount of information from CCTV cameras installed in vehicle interiors.The use of theoretical research methods helped to create an intelligent system capable to analyse and compare the number of tickets issued to passengers with the actual number of passengers transported on Samara city public transport route, the results were displayed through a graphical user interface. It was possible to reveal dependences of the number of unaccounted passengers on the route on the amount of passenger flow and the time of day.A possibility of using the proposed intelligent system in commuter trains was noted, provided that the cars are equipped with video surveillance cameras.

110 Evaluation of Computer Vision to Analyze Beef Cattle Feeding Behavior

Abstract Leveraging computer vision (CV) methods to study pen-fed cattle feeding behavior presents several advantages, including monitoring animal health and identifying feed efficient animals. This research employed a region-based convolutional neural network (RCNN) combined with the common objects in context (COCO) dataset for automatic livestock recognition using CV techniques in experimental feedlot pens. Thirty Angus-influenced steers were allocated in one pen with four automated feed intake systems (AFIS; Vytelle SENSE). CV data were recorded during daylight hours using a webcam (Microsoft LifeCam Cinema) with a resolution of 1280x720 pixels at ten frames per second connected to a video surveillance camera software (Contaware, Switzerland). The CV dataset obtained by the cameras, Figure 1, was benchmarked with feeding behavior data obtained from observed annotations (OA) and AFIS. The CV model utilized was a Mask-RCNN with pre-trained weights on the COCO dataset. The Mask RCNN algorithm can identify and locate multiple objects, objects of different scales, and overlapping objects within an image. The COCO dataset is a large and diverse set of annotated images that can be used for training and evaluating object detection models. The fully trained Mask-RCNN model takes each video frame as input and outputs the pixel segmentation of each object detected. For the CV analyses, each animal bounding box was associated with a score threshold, and each pixel within was assigned a probability threshold; both ranging from 0 to 100. The higher the score and probability thresholds are, the fewer the boxes and pixels will be marked as the animal boxing shape, resulting in less feeding detection from the CV system. In contrast, a low score and probability thresholds produce many predictions, including false detections, which might be misleading. Therefore, a dataset analysis was conducted to optimize the best threshold combination. We evaluated the accuracy and precision of the CV model compared with OA and AFIS by analyzing a dataset section of feeding events of one feed bunk using a 0.5 score and 0.5 probability thresholds. We obtained the values for the true positive (TP), false positive (FP), true negative (TN), and false negative (FN) to calculate the accuracy = [TP+TN]/[TP+TN+FP+FN], and precision = [TP]/[TP+FP], Table 1. As a result, the CV model achieved 95.39% and 99.82% accuracy and 93.60% and 99.90% precision when compared with OA and AFIS, respectively. Our findings showcase the promising capabilities of employing the Mask-RCNN algorithm and COCO datasets for detecting beef cattle feeding behavior in feedlot pens. This approach holds significant value for the cattle industry by enabling precise monitoring and analysis of individual animal behavior within feedlot pens, facilitating early detection of behavioral abnormalities.

YOLOv5-MS: Real-Time Multi-Surveillance Pedestrian Target Detection Model for Smart Cities.

Intelligent video surveillance plays a pivotal role in enhancing the infrastructure of smart urban environments. The seamless integration of multi-angled cameras, functioning as perceptive sensors, significantly enhances pedestrian detection and augments security measures in smart cities. Nevertheless, current pedestrian-focused target detection encounters challenges such as slow detection speeds and increased costs. To address these challenges, we introduce the YOLOv5-MS model, an YOLOv5-based solution for target detection. Initially, we optimize the multi-threaded acquisition of video streams within YOLOv5 to ensure image stability and real-time performance. Subsequently, leveraging reparameterization, we replace the original BackBone convolution with RepvggBlock, streamlining the model by reducing convolutional layer channels, thereby enhancing the inference speed. Additionally, the incorporation of a bioinspired "squeeze and excitation" module in the convolutional neural network significantly enhances the detection accuracy. This module improves target focusing and diminishes the influence of irrelevant elements. Furthermore, the integration of the K-means algorithm and bioinspired Retinex image augmentation during training effectively enhances the model's detection efficacy. Finally, loss computation adopts the Focal-EIOU approach. The empirical findings from our internally developed smart city dataset unveil YOLOv5-MS's impressive 96.5% mAP value, indicating a significant 2.0% advancement over YOLOv5s. Moreover, the average inference speed demonstrates a notable 21.3% increase. These data decisively substantiate the model's superiority, showcasing its capacity to effectively perform pedestrian detection within an Intranet of over 50 video surveillance cameras, in harmony with our stringent requisites.