Network Of Video Cameras Research Articles

Advances and Challenges in Pest Management for Protected Cultivation in India: Integrative Approaches and Future Prospects

Poly house farming in India has transformed agriculture by enabling year-round crop production and protecting crops from adverse weather. However, the controlled environment of poly houses represents unique challenges in pest management, making it crucial to implement effective strategies to maintain crop health and yield. Integrated Pest Management (IPM) offers a comprehensive approach that combines cultural, biological and chemical practices to control pests while minimizing environmental impact. The advantages of protected cultivation include increased productivity, improved produce quality and efficient resource utilization. Despite these benefits, poly house farming faces limitations such as high initial costs, the need for meticulous planning and ongoing crop protection requirements. Pest identification is vital for effective management, as misidentification can lead to inappropriate control measures. Common pests in poly houses include insects, slugs, mites, nematodes, pathogens and weeds. Pest monitoring strategies such as scouting, using monitoring tools and record-keeping are essential for timely and effective management interventions. Management strategies encompass physical, biological and chemical methods. Physical approaches include the use of sticky and pheromone traps, while biological control involves the release of natural predators and entomopathogens. Chemical management involves both biorational pesticides and conventional chemicals, with IPM emphasizing the integration of these methods to reduce reliance on harmful chemicals. Preventive measures like insect-proof screens and soil solarization, along with curative measures and advanced technologies like video camera networks and decision support systems, are pivotal for effective pest control. Future prospects include improving technology standards, enhancing computerized control systems, and fostering research and government support to advance protected cultivation in India.

Implementation and Application of a Regional Fireball Monitoring Network Based on All-sky Camera Networking

Fireball monitoring network is the main equipment for monitoring impact from small-size near-Earth objects (NEOs), and determining meteorite fall locations. In this paper, a monitoring system using a multi-station distributed all-sky video camera network is proposed, and a regional prototype system has been constructed in Jiangsu and surrounding areas. The main processes of fireball monitoring were implemented, including network control, video capture, data processing, trajectory determination, and orbit calculation. After running for 1 yr, the results show that the limiting apparent magnitude - 1.0 for meteors in the video, and the absolute magnitude exhaustive detection regime can reach around - 2.5. The flux of fireballs is found to be 2.68×10−7 km−2· h−1. The percentages of streams and sporadic meteors are 46% and 54%, respectively, and the fraction of cometary orbits equals 72.9%, while for asteroidal-type it is 27.1%. The statistical results are close to those of the international meteor monitoring networks, which verified the monitoring capability of the network system in practical operation.

An observing campaign to search for meteoroids of Bennu at Earth

An observing campaign was conducted in the Southern Hemisphere using low-light video camera triangulation to measure the trajectories and orbits of meteoroids with a possible origin at asteroid Bennu. New CAMS (Camera for Allsky Meteor Surveillance) video camera networks were established in Australia, Chile, and Namibia, and networks in New Zealand and South Africa were expanded. During observing periods in September 2019, 2020, and 2021, we measured 7672, 4936, and 5890 orbits, respectively. Based on the non-detection of predicted meteoroid trail encounters, Bennu's meteoroid production rate was <1.5 kg/s during 1500–1800 CE. Indeed, the current production rate is many orders of magnitude lower. Bennu may have an associated annual meteoroid stream of much older ejecta at a particle flux density of ≤1.3 × 10−6 km−2 h−1, based on seven Bennu-like orbits detected during the first three years of observations.

Совершенствование методов визуального осмотра зданий и инженерных сооружений путем внедрения технологий компьютерного зрения и интеллектуальной обработки данных

For the safe operation of engineering facilities, periodic surveys are carried out to assess their technical condition. To form an expert opinion and establish cause-and-effect relationships of changes in the state of an object from the influence of external factors, the first in the sequence of work, according to the terms of reference, is a visual inspection of the object or a visual inspection. As a result of visual inspection, it is possible to fix the occurrence of cracks in structures, the formation of a corrosion layer that damages structures, violations of the protective layer of the surfaces under study, violations of the paintwork of elements and parts of the structure of the object, etc. In order to improve the visual inspection technique, a hardware-software complex has been developed that makes it possible to apply computer vision technologies to provide automated visual monitoring of an object and analyze the obtained images by using intelligent algorithms for their processing. The use of a hardware-software complex for automated visual monitoring of an object in real time will allow performing: automated visual monitoring of a plurality of structural elements of an object at the same time due to a stationary installation of a network of video cameras at the object; intelligent analysis of the stream of video im-ages in real time, as a result of which information is given not only on the defects present, found on the object, but on the dynamics of their changes; identify defects in structural elements in hard-to-reach places where they cannot be visually determined, but it is possible to install a video camera; minimize the human factor.

Meteor showers from known long-period comets

What long-period comets with orbital periods >250 years cause detectable meteor showers on Earth? Low-light video cameras are used to track the motion of +4 to −5 magnitude meteors in our atmosphere by triangulation and calculate the meteoroid orbit in space. In recent years, the CAMS (Cameras for Allsky Meteor Surveillance) low-light video camera network was greatly expanded and, together with other video networks, now has increased the total video meteoroid orbit database to over 2.2 million orbits. Here, we searched this database for meteor showers associated with known long-period comets. Previously, five associations were known. Now, we find 14, as well as six uncertain but likely associations. These showers show a change of longitude of perihelion with node that is a strong function of inclination. Showers of longer duration show a steeper magnitude distribution index, presumably due to aging of the meteoroid population. Showers are generally detected only if the orbital period of the comet is less than 4000 years and the Earth-Comet orbital miss distance is ≤0.10 AU. The lack of an associated meteor shower sets lower limits on the orbital period of poorly observed comets.

Қазақстан жолдарындағы интеллектуальды көлік жүйелерін қолдану

The progress of work on the creation of an "intelligent transport system" in Kazakhstan, the development and modernization of the road industry will be reported. The economic and social effectiveness of several transport corridors and new projects were analyzed. Transport corridors of foreign countries, methods of providing logistics services were presented. In his address, the head of state paid special attention to the issues of digitalization of all spheres of life of the population, including the transport sector of Kazakhstan. Currently, the Ministry of investment and development of the Republic of Kazakhstan is working on the creation of an "intelligent transport system" within the framework of the state program «Digital Kazakhstan» (ITS). ITS goal is to systematically integrate transport infrastructure, transport devices and users with modern information and communication technologies aimed at improving the safety and efficiency of the transport process. The concept will include 11 components in ITS. One of them is a special automated measuring instruments (UAVs), which are installed in the main automobile corridors. This device is designed to measure moving vehicles without contact and eliminate unjustified stops. This year, it is planned to put into operation 10 units of RSPP, and by 2020 it is planned to increase their number to 46 units. It is also planned to introduce a traffic management system that will inform drivers about the situation on the roads, a system for forecasting and analyzing climatic conditions, which is carried out through a network of weather stations along the roads, a network of video monitoring and special video cameras for detecting traffic violations, toll road systems that cover the cost of maintenance of the national road network, etc. They will be introduced in stages until 2021. The implementation of this project will reduce road deaths, increase the volume of transit traffic and the speed of logistics services, fully cover the main highways with measuring instruments and create favorable conditions for drivers on the roads. Such systems are already operating in South Korea, Japan, Australia, Europe and the United States.

Criminal Intention Detection at Early Stages of Shoplifting Cases by Using 3D Convolutional Neural Networks

Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks generate vast amounts of data, and the surveillance staff cannot process all the information in real-time. Human sight has critical limitations. Among those limitations, visual focus is one of the most critical when dealing with surveillance. For example, in a surveillance room, a crime can occur in a different screen segment or on a distinct monitor, and the surveillance staff may overlook it. Our proposal focuses on shoplifting crimes by analyzing situations that an average person will consider as typical conditions, but may eventually lead to a crime. While other approaches identify the crime itself, we instead model suspicious behavior—the one that may occur before the build-up phase of a crime—by detecting precise segments of a video with a high probability of containing a shoplifting crime. By doing so, we provide the staff with more opportunities to act and prevent crime. We implemented a 3DCNN model as a video feature extractor and tested its performance on a dataset composed of daily action and shoplifting samples. The results are encouraging as the model correctly classifies suspicious behavior in most of the scenarios where it was tested. For example, when classifying suspicious behavior, the best model generated in this work obtains precision and recall values of 0.8571 and 1 in one of the test scenarios, respectively.

EAgLE: Equivalent Acoustic Level Estimator Proposal.

Road infrastructures represent a key point in the development of smart cities. In any case, the environmental impact of road traffic should be carefully assessed. Acoustic noise is one of the most important issues to be monitored by means of sound level measurements. When a large measurement campaign is not possible, road traffic noise predictive models (RTNMs) can be used. Standard RTNMs present in literature usually require in input several information about the traffic, such as flows of vehicles, percentage of heavy vehicles, average speed, etc. Many times, the lack of information about this large set of inputs is a limitation to the application of predictive models on a large scale. In this paper, a new methodology, easy to be implemented in a sensor concept, based on video processing and object detection tools, is proposed: the Equivalent Acoustic Level Estimator (EAgLE). The input parameters of EAgLE are detected analyzing video images of the area under study. Once the number of vehicles, the typology (light or heavy vehicle), and the speeds are recorded, the sound power level of each vehicle is computed, according to the EU recommended standard model (CNOSSOS-EU), and the Sound Exposure Level (SEL) of each transit is estimated at the receiver. Finally, summing up the contributions of all the vehicles, the continuous equivalent level, Leq, on a given time range can be assessed. A preliminary test of the EAgLE technique is proposed in this paper on two sample measurements performed in proximity of an Italian highway. The results will show excellent performances in terms of agreement with the measured Leq and comparing with other RTNMs. These satisfying results, once confirmed by a larger validation test, will open the way to the development of a dedicated sensor, embedding the EAgLE model, with possible interesting applications in smart cities and road infrastructures monitoring. These sites, in fact, are often equipped (or can be equipped) with a network of monitoring video cameras for safety purposes or for fining/tolling, that, once the model is properly calibrated and validated, can be turned in a large scale network of noise estimators.

CrowdTracking: Real-Time Vehicle Tracking Through Mobile Crowdsensing

Traditionally, vehicle tracking is accomplished using predeployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this paper, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photographs, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photographs of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle’s actual trajectory and 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 m error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set and experimental results validate its effectiveness and efficiency.

Dynamic networks of fighting and mating in a wild cricket population

Reproductive success is often highly skewed in animal populations. Yet the processes leading to this are not always clear. Similarly, connections in animal social networks are often nonrandomly distributed, with some individuals with many connections and others with few, yet whether there are simple explanations for this pattern has not been determined. Numerous social interactions involve dyads embedded within a wider network. As a result, it may be possible to model which individuals accumulate social interactions through a more general understanding of the social network's structure, and how this structure changes over time. We analysed fighting and mating interactions across the breeding season in a population of wild field crickets under surveillance from a network of video cameras. We fitted stochastic actor-oriented models to determine the dynamic process by which networks of cricket fighting and mating interactions form, and how they co-influence each other. We found crickets tended to fight those in close spatial proximity to them and those possessing a mutual connection in the fighting network, and heavier crickets fought more often. We also found that crickets that mated with many others tended to fight less in the following time period. This demonstrates that a mixture of spatial constraints, characteristics of individuals and characteristics of the immediate social environment are key for determining social interactions. The mating interaction network required very few parameters to understand its growth and thus its structure; only homophily by mating success was required to simulate the skew of mating interactions seen in this population. This demonstrates that relatively simple, but dynamic, processes can give highly skewed distributions of mating success. • Social network connections and patterns of mating success are both strongly skewed. • We modelled dynamic social (fighting) and mating networks in wild crickets. • The fighting network was influenced by proximity, sex, mass and mutual connections. • Only positive assortment by mating success was required to model the mating network. • In many populations, simple, dynamic processes may explain strong skews in fitness.

Researches on Inertial Mechatronic Motion Analysis Systems, Based on MEMS

Abstract The aim of this paper is to provide an introduction into the inertial motion analysis field, focusing its attention on the analysis that is performed using modern mechatronic inertial motion capture systems, highlighting both the advantages and drawbacks of using such a system and outlining the main constituent elements of these systems as well as the necessary steps to be carried out in order to be able to accomplish such analysis. A modern mechatronic inertial motion analysis system’s evolution it’s based on MEMS (Micro-Electro-Mechanical-Systems) sensory network, each of which contains a combination of accelerometer, gyroscope, magnetometer. The signals from these MEMS are processed, by a microcontroller, using advanced algorithms in order to provide accurate data regarding body’s segments kinematics, global positioning and magnetic field. Those data are being transferred to a biomechanical model for the analysis. Despite its tremendous advantages, such as portability and real-time analysis capabilities, over alternative motion analysis systems, such as optical or mechanical systems, that based on external transmitters and/or video camera networks, restricting their use to special laboratory conditions and/or large workspace, mechatronic inertial systems are not based on an external infrastructure, they present a major disadvantage, namely the positional drift. The estimation of the human subject body’s segments positions and orientation, absolutely requires an initial calibration procedure, called „sensor to segment calibration”, that starts from the positional and orientation information received from the MEMS network, which are then transferred onto a biomechanical (scaled) model of the human body.

Automatic matching of construction onsite resources under camera views

When a video camera network is placed on a construction site to monitor onsite activities, construction resources, such as equipment and worker, might be captured by two or more cameras at the same time. Therefore, it is important to conduct the matching to identify whether the resources captured into separate camera views refer to the same one on the site. Otherwise, it leads to the repetitive counting, when analyzing the onsite resources utilization automatically. This paper proposes a novel matching method that relies on the construction site visual features and the spatial relationships of onsite construction resources as the matching cues. Specifically, the method first searches the potential matching candidates between two camera views following their epipolar constraints. Then, the triangular coordinates of these candidates are calculated based on their locations in the triangular mesh of each camera view. This way, the matching of multiple construction resources between two camera views could be converted to a combinatorial optimization problem and solved with the Hungarian algorithm. The proposed method has been tested with the images and videos captured from real construction sites. The test results showed that the average matching accuracy could reach 93%.

Progressive boosting for class imbalance and its application to face re-identification

In practice, pattern recognition applications often suffer from imbalanced data distributions between classes, which may vary during operations w.r.t. the design data. For instance, in many video surveillance applications, e.g., face re-identification, the face individuals must be recognized over a distributed network of video cameras. An important challenge in such applications is class imbalance since the number of faces captured from an individual of interest is greatly outnumbered by those of others. Two-class classification systems designed using imbalanced data tend to recognize the majority (negative) class better, while the class of interest (positive class) often has the smaller number of samples. Several data-level techniques have been proposed to alleviate this issue, where classifier ensembles are designed with balanced data subsets by up-sampling positive samples or under-sampling negative samples. However, some informative samples may be neglected by random under-sampling and adding synthetic positive samples through up-sampling adds to training complexity. In this paper, a new ensemble learning algorithm called Progressive Boosting (PBoost) is proposed that progressively inserts uncorrelated groups of samples into a Boosting procedure to avoid loosing information while generating a diverse pool of classifiers. In many real-world recognition problems, the samples may be regrouped using some application-based contextual information. For example, in face re-identification applications, facial regions of a same person appearing in a camera field of view may be regrouped based on their trajectories found by face tracker. From one iteration to the next, the PBoost algorithm accumulates these uncorrelated groups of samples into a set that grows gradually in size and imbalance. Base classifiers are trained on samples selected from this set and validated on the whole set. Consequently, PBoost is more robust when the operational data may have unknown and variable levels of skew. In addition, the computation complexity of PBoost is lower than Boosting ensembles in literature that use under-sampling for learning from imbalanced data because not all of the base classifiers are validated on all negative samples. The new loss factor used in PBoost avoids biasing performance towards the negative class. Using this loss factor, the weight update of samples and classifier contribution in final predictions are set according to the ability of classifiers to recognize both classes. The proposed approach was validated and compared using synthetic data and videos from the Faces In Action, and COX dataset that emulate face re-identification applications. Results show that PBoost outperforms state of the art techniques in terms of both accuracy and complexity over different levels of imbalance and overlap between classes.

Improved Algorithm for Object Tracking in Video Camera Network

Objectives: To develop a novel approach for tracking the object in multiple camera environments. The object tracking in situation of suspect person is the challenging task for a researcher for a long time. The situation get worst if the object hided beside any other object, or if the object moves from the range of camera span Methods/Statistical Analysis: The object is tracked using Optical Flow Estimation techniques in which magnitude along the gradient direction is observed. The threshold magnitude comparison of the images detects the desired target. Findings: Detection parameters in the our proposed methods such as SIMILARITY, F1, PRECISION, RECALL are better in comparison with the existing methods in existing methods viz RADCT, MSDE, SDE and SSD. The proposed algorithm can easily detect object even if it is either hided or it moves to the frame of camera 2, or any other camera installed within the same camera network. Application/ Improvements: The proposed methods tracked and detects the suspected object target for the application of safety and security system more accurately and better ease of implementation than other existing motion detection system such as RADCT, MSDE, SDE and SSD.

Robust watch-list screening using dynamic ensembles of SVMs based on multiple face representations

Still-to-video face recognition (FR) is an important function in video surveillance (VS), where faces captured over a network of video cameras are matched against reference stills of target individuals. Screening faces against a watch-list is a challenging VS application because the appearance of faces varies due to changing capture conditions and operational domains. The facial models used for matching may not be representative of faces captured with video cameras because they are typically designed a priori with only one reference still. In this paper, a multi-classifier framework is proposed for robust still-to-video FR based on multiple and diverse face representations of a single reference face still. During enrollment of a target individual, the single reference face still is modeled using an ensemble of SVM classifiers based on different patches and face descriptors. Multiple feature extraction techniques are applied to patches isolated in the reference still to generate a diverse SVM pool that provides robustness to common nuisance factors (e.g., variations in illumination and pose). The estimation of discriminant feature subsets, classifier parameters, decision thresholds, and ensemble fusion functions is achieved using the high-quality reference still and a large number of faces captured in lower-quality video of non-target individuals in the scene. During operations, the most competent subset of SVMs is dynamically selected according to capture conditions. Finally, a head-face tracker gradually regroups faces captured from different people appearing in a scene, while each individual-specific ensemble performs face matching. The accumulation of matching scores per face track leads to a robust spatiotemporal FR when accumulated ensemble scores surpass a detection threshold. Experimental results obtained with the Chokepoint and COX-S2V datasets show a significant improvement in performance w.r.t. reference systems, especially when individual-specific ensembles (1) are designed using exemplar-SVMs rather than one-class SVMs and (2) exploit score-level fusion of local SVMs (trained using features extracted from each patch), rather than using either decision-level or feature-level fusion with a global SVM (trained by concatenating features extracted from patches).

Individual‐specific management of reference data in adaptive ensembles for face re‐identification

During video surveillance, face re-identification allows recognition and targeting of individuals of interest from faces captured across a network of video cameras. In such applications, face recognition is challenging because faces are captured under limited spatial and temporal constraints. In addition, facial models for recognition are commonly designed using a limited number of representative reference samples from faces captured under specific conditions, regrouped into facial trajectories. Given new reference samples (provided by an operator or through some self-updating process), updating facial models may allow maintenance of a high level of performance over time. Although adaptive ensembles have been successfully applied to robust modelling of an individual's facial appearance, reference data samples from a trajectory must be stored for validation. In this study, a memory management strategy based on Kullback-Leiber (KL) divergence is proposed to rank and select the most relevant validation samples over time in adaptive individual- specific ensembles. When new reference samples become available for an individual, updates to the corresponding ensemble are validated using a mixture of new and previously-stored samples. Only the samples with the highest KL divergence are preserved in memory for future adaptations. This strategy is compared with reference classifiers using videos from the face in action data. Simulation results show that the proposed strategy tends to select discriminative samples from wolf-like individuals for validation. It allows maintenance of a high level of performance, while reducing the number of samples per individual by up to 80%.

Design and implementation of a wireless video camera network for coastal erosion monitoring

The short-term rate of coastal erosion and recession has been observed at island shoreline bluffs near waterways among Boston Harbor, Massachusetts, USA. This erosion has been hypothesized partially related to waves from high-speed wakes. Recording the physical erosion events during extreme high waves is significant to evaluate the dynamics of bluff erosion and to document these short-term processes. Still and motion imagery are important media to observe rare and extreme events in ecology, geology, and environmental condition. The study of coastal erosion requires recording devices for these modalities capable of long-term, low-cost, low-power operation with low maintenance, and with the ability to support a large dynamic range in both time and space. We describe recent work in the development of a wireless video camera network for an ecosystem observation platform. These cameras are enclosed in weatherproof housings and supported by solar energy harvesting. The cameras are Internet-enabled and thus live video can be accessed remotely. Video streams are transmitted via wireless network, and delivered to and stored at a remote server. This system has been functional as designed since installation in October 2012 on Thompson Island, Massachusetts, and is expected to operate indefinitely. To date, a number of erosion-related events have been successfully captured. This platform has shown the potential to be used in a large scale for a variety of environmental monitoring studies.

Game-theoretic surveillance over arbitrary floor plan using a video camera network

Coordinated multi-resolution tracking over arbitrary floor plan is addressed using a game-theoretic approach. An enhanced radial sweep algorithm is devised to find the polygon of visibility at any point on or inside a polygon that contains vision-obstructing polygonal entities. By sampling the edges of a polygon and edges of any polygonal hole inside that polygon, a two-pass 0–1 programming process is formulated to find a near-optimal set of camera samples that can dynamically cover, at a high probability, area under surveillance in the presence of camera handoffs. Radius Multiplier is introduced to handle partial visibility and is set to 1 by default to avoid insolvability of 0–1 programming problems. As a remedy to excessive redundancy triggered by camera clustering, we set camera redundancy to a fixed value of 3 for any block with concave Valid Area. Branch-and-cut algorithm is employed to solve 0–1 programming problems. Assigning a fixed value to Camera Redundancy of blocks with concave Valid Area, setting Radius Multiplier to a nonzero value, and utilizing secondary utility yielded better simulation results for various types of floor plans. Raising Camera Redundancy of blocks with non-concave Valid Area contributed to performance boost and in the meantime, increased the number of cameras needed.

New Information Technology Tools for a Medical Command System for Mass Decontamination

In a mass decontamination during a nuclear, biological, or chemical (NBC) response, the capability to command, control, and communicate is crucial for the proper flow of casualties at the scene and their subsequent evacuation to definitive medical facilities. Information Technology (IT) tools can be used to strengthen medical control, command, and communication during such a response. Novel IT tools comprise a vehicle-based, remote video camera and communication network systems. During an on-site verification event, an image from a remote video camera system attached to the personal protective garment of a medical responder working in the warm zone was transmitted to the on-site Medical Commander for aid in decision making. Similarly, a communication network system was used for personnel at the following points: (1) the on-site Medical Headquarters; (2) the decontamination hot zone; (3) an on-site coordination office; and (4) a remote medical headquarters of a local government office. A specially equipped, dedicated vehicle was used for the on-site medical headquarters, and facilitated the coordination with other agencies. The use of these IT tools proved effective in assisting with the medical command and control of medical resources and patient transport decisions during a mass-decontamination exercise, but improvements are required to overcome transmission delays and camera direction settings, as well as network limitations in certain areas.

Design and implementation of adaptive WLAN mesh networks for video surveillance

This paper presents a novel design and practical experiments of adaptive wireless mesh networks based on 802.11 based Wireless Local Area Networks for supporting public video surveillance. A network of video cameras and sensors can be equipped with wireless communication modules to form wireless surveillance at only a small expense of cost and labor. However, large and redundant multimedia data transmitted through the wireless medium pose challenging problems such as decrease in Quality of Service (QoS) and system reliability. To support seamless transmission of surveillance data through wireless means, we propose a wireless surveillance network design based on the protocols and functions of IEEE 802.11 mesh standard. One of the key functions of our adaptive mesh network is the “Multi-Gateway Routing with Congestion Avoidance”, which provides enhanced QoS support via adaptive congestion control. Network congestion is predicted in a distributed manner and amounts of data traffic transmitted to the congested path are redirected to multiple gateways. Redirection of traffic flows are made in such a way to provide quicker transmission for more time critical packets. Experimental studies via practical testbed implementation and simulation via NS-3 are conducted to prove the superiority of our proposed scheme.