Intelligent Traffic Surveillance Research Articles

Automatic Video Traffic Surveillance System with Number Plate Character Recognition Using Hybrid Optimization-Based YOLOv3 and Improved CNN

Recently, the increased count of surveillance cameras has manipulated the demand criteria for a higher effective video coding process. Moreover, the ultra-modern video coding standards have appreciably enhanced the efficiency of video coding, which has been developed for gathering common videos over surveillance videos. Various vehicle recognition techniques have provided a challenging and promising role in computer vision applications and intelligent transport systems. In this case, most of the conventional techniques have recognized the vehicles along with bounding box depiction and thus failed to provide the proper locations of the vehicles. Moreover, the position details have been vigorous in terms of various real-time applications trajectory of vehicle’s motion on the road as well as movement estimation. Numerous advancements have been offered throughout the years in the traffic surveillance area through the random propagation of intelligent traffic video surveillance techniques. The ultimate goal of this model is to design and enhance intelligent traffic video surveillance techniques by utilizing the developed deep learning techniques. This model has the ability to handle video traffic surveillance by measuring the speed of vehicles and recognizing their number plates. The initial process is considered the data collection, in which the traffic video data is gathered. Furthermore, the vehicle detection is performed by the Optimized YOLOv3 deep learning classifier, in which the parameter optimization is performed by using the newly recommended Modified Coyote Spider Monkey Optimization (MCSMO), which is the combination of Coyote Optimization Algorithm (COA) and Spider Monkey Optimization (SMO). Furthermore, the speed of the vehicles has been measured from each frame. For high-speed vehicles, the same Optimized YOLOv3 is used for detecting the number plates. Once the number plates are detected, plate character recognition is performed by the Improved Convolutional Neural Network (ICNN). Thus, the information about the vehicles, which are violating the traffic rules, can be conveyed to the vehicle owners and Regional Transport Office (RTO) to take further action to avoid accidents. From the experimental validation, the accuracy and precision rate of the designed method achieves 97.53% and 96.83%. Experimental results show that the proposed method achieves enhanced performance when compared to conventional models, thus ensuring the security of the transport system.

VEHICLE DETECTION & TRACKING

Any machine that moves people or goods qualifies as a vehicle. Vehicles include automobiles, bicycles, buses, aircraft, space shuttles, and a wide variety of others. A practical application of machine learning principles is vehicle detection and vehicle type recognition, which is directly applicable for a variety of operations in a traffic surveillance system and contributes to an intelligent traffic surveillance system. The processing of automatic vehicle identification and recognition using datasets of static images will be covered in this study. The surveillance system features moving vehicle detection and recognition, vehicle count, and permission verification with the organisation. Considering that algorithms are crucial to any machine learning programme, it is crucial that we select the optimum model for our project.

Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Classification and Counting of Vehicle using Image Processing Techniques

Abstract: In this research work, we explore the vehicle detection technique that can be used for traffic surveillance systems. This system works with the integration of CCTV cameras for detecting the vehicle. Vehicle Tracking and Counting Monitoring highway traffic is an important application of computer vision research. In this paper, we analyze congested highway situations where it is difficult to track individual vehicles in heavy traffic because vehicles either occlude each other. In this we focus on the issues to propose a viable solution and we apply the vehicle detection results to multi object tracking and vehicle counting. Vehicle detection and vehicle type recognition is a practical application of machine learning concepts and is directly applicable for various operations in a traffic surveillance system contributing to an intelligent traffic surveillance system. This paper will introduce the processing of automatic vehicle detection and recognition using static image datasets. Further using the same technique, we shall improvise vehicle detection by using live CCTV surveillance. The surveillance system includes detection of moving vehicles and recognizing them, counting number of vehicles.

An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization.

The transportation industry is crucial to the realization of a smart city. However, the current growth in vehicle numbers is not being matched by an increase in road capacity. Congestion may boost the number of accidents, harm economic growth, and result in higher gas emissions. Currently, traffic congestion is seen as a severe threat to urban life. Suffering as a result of increased car traffic, insufficient infrastructure, and inefficient traffic management has exceeded the tolerance limit. Since route decisions are typically made in a short amount of time, the visualization of the data must be presented in a highly conceivable way. Also, the data generated by the transportation system face difficulties in processing and sometimes lack effective usage in certain fields. Hence, to overcome the challenges in computer vision, a novel computer vision-based traffic management system is proposed by integrating a wireless sensor network (WSN) and visual analytics framework. This research aimed to analyze average message delivery, average latency, average access, average energy consumption, and network performance. Wireless sensors are used in the study to collect road metrics, quantify them, and then rank them for entry. For optimization of the traffic data, improved phase timing optimization (IPTO) was used. The whole experimentation was carried out in a virtual environment. It was observed from the experimental results that the proposed approach outperformed other existing approaches.

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper.

A Semantic Extraction and Analysis for Traffic Density Using Traffic Images: A Critical Review

Population growth in large cities has contributed to the increase in vehicles' number, leading to the traffic congestion problem. Incompetent traffic supervision could squander an inconsiderable number of man-hours and might lead to fatal consequences. Therefore, intelligent traffic surveillance systems have to carry more significant roles in highway monitoring and traffic management system throughout the years. Although vehicle detection and classification methods have evolved rapidly throughout the years, they still lack high-level reasoning. Accurate and precise vehicle recognition and classification are still insufficient to develop an intelligent and reliable traffic system. There is a demand to increase the confidence in image understanding and effectively extract the images conformed to human perception and without human interference. This paper attempts to summarize a review on several methods that semantically extract and analyze traffic density with image processing techniques. Three (3) methods that have been selected to be discussed in this paper are semantic analysis of traffic video using image understanding, mining semantic context details of traffic scene, and integrating vision and language in semantic description of traffic events from image sequences. Each method is discussed thoroughly, and their outstanding issue is deliberated in this paper.

Intelligent traffic analysis: A heuristic high-dimensional image search algorithm based on spatiotemporal probability for constrained environments

However, most heuristic algorithms are solely based on image features, due to the lack of diverse attributes of samples. Rather than rely on the existing datasets, this paper fully processes the original data. Firstly, the original videos were collected from several intelligent traffic surveillance cameras (monitoring nodes). Then, the three categories of objects in all videos were classified by a video image classifier. For each object in the critical class of cars, four attributes were identified, including ID, region of interest (ROI), sampling time and sampling position (position of sampling node). Next, the moving attitude (i.e. moving direction and speed) of each object was estimated through moving target tracking, and added to the preprocessed data. On this basis, a basic hyper-spherical hash search algorithm was created for the initial identification of similar objects. Furthermore, 93% of the objects were accurately identified through the recognition of license plate. Thereafter, a spatiotemporal correlation Bayesian network (STCBN) was established to predict the probability of each object to appear at a certain node in a period of time. Based on the probability, the heuristic factor was designed and used to enhance the basic hyper-spherical hash search algorithm. The enhancement improves the search performance, while reducing the environmental requirements of a single node. Therefore, the enhanced algorithm can be deployed effectively in edge computing systems. Our method was proved effective through experiment on a number of actual monitoring nodes.

A Viewpoint Aware Multi-Task Learning Framework for Fine-Grained Vehicle Recognition

The goal of fine-grained vehicle recognition is to identify the exact subtype of the vehicle from a given image. It plays an important role in the intelligent traffic surveillance system. Although fine-grained vehicle recognition has attracted more and more research interest, it remains as an open problem for vehicle images taken from arbitrary viewpoints. In this study, we present a one-stage deep multi-task learning framework for fine-grained vehicle recognition in traffic surveillance, which performs the fine-grained vehicle recognition and viewpoint estimation simultaneously. In the proposed framework, the fine-grained vehicle recognition is the main task which classifies images into different types, and the viewpoint estimation task is the auxiliary task to learn helpful viewpoint-aware features for improving the main task. We evaluate our method on two common used large-scale fine-grained vehicle recognition datasets, including BoxCars116k and CompCars. The experimental results testify that the proposed multi-task framework can improve the accuracy by incorporating the viewpoint information of the vehicles. In comparison to the state-of-the-art, our approach goes beyond, only requiring 3D bounding box for the training phase, which is important for future inferences using the trained model.

A Robust Vehicle Detection Scheme for Intelligent Traffic Surveillance Systems in Smart Cities

Accurately obtaining road vehicle information is important in intelligent traffic surveillance systems for smart cities. Especially smart vehicle detection is recognized as the critical research issue of intelligent traffic surveillance systems. In this paper, a robust real-time vehicle detection method for the system is proposed. The method combines background subtraction model MOG2(Mixture of Gaussians) with a modified SqueezeNet model (H-SqueezeNet). The MOG2 model is utilized to create scale-insensitive Region of Interest (RoIs) from video frames. H-SqueezeNet is then proposed to accurately identify vehicle category. The effectiveness of the method was verified in CDnet2014 dataset, UA-DETRAC dataset and video data from a traffic intersection in Suzhou, China. The experiment results show that the method can achieves excellent detection accuracy in traffic surveillance systems, and achieve an average detection speed of 39.1 FPS.

Vehicle Speed Measurement Based on Binocular Stereovision System

In this paper, we propose a novel stereovision-based system for vehicle speed measurement. Our system is set in a fixed location to capture two view stereo videos for passing vehicles by using a calibrated binocular stereovision system. An important feature of this system is the use of an optimized single shot multibox detector network that can efficiently detect license plates in the captured two view stereo videos. Then, the system performs only on the detected license plate area to extract stereo matching point pairs rapidly. The world coordinates of 3D points corresponding to each stereo matching point pair are calculated. Abnormal 3D points are removed in accordance with distance distribution. The remaining 3D point closest to the license plate center is finally selected as the exact vehicle location of the current frame. Vehicle speed is measured by dividing the distance between two selected 3D points by frame intervals. Vehicle trajectory is depicted by connecting multiple selected 3D points. Our proposed system performs non-intrusive and stealth measurement in intelligent traffic surveillance and overcomes the challenges of speed measurement for multiple vehicles on multiple lanes in different motions simultaneously. Experimental results demonstrate its accuracy. Our system has a speed measurement error range of [-1.6, +1.1] km/h and a maximum error rate of 3.80%, which are within the [-3, +2] km/h error limit of several countries' regulatory authorities and the 6% error rate limit of China national standard GB/T 21255-2007.

Efficient moving vehicle detection for intelligent traffic surveillance system using optimal probabilistic neural network

The vehicle detection system plays an essential role in the traffic video surveillance system. Video communication of these traffic cameras over real-world limited bandwidth networks can frequently suffer network congestion. The objective of this paper is to develop an effective method for moving vehicle detection problems that can find high quality solutions (with respect to detection accuracy) at a high convergence speed. To achieve this objective, we propose a method that hybridises the cuckoo search (CS) with Opposition-based learning (OBL), where OBL is improve the performance of the CS algorithm while optimising the weights of the standard PNN model. The proposed system mainly consists of two modules such as: 1) design novel OCS-PNN model; 2) moving vehicle detection using OCS-PNN model. The algorithm is tested on three standard video dataset. For instance, the proposed method achieved the maximum precision of 94%, F-measure of 94% and similarity of 94%.

Research on Vehicle Detection Algorithms in Surveillance Video Images

Intelligent traffic video surveillance system, including vehicle inspection, vehicle tracking and license plate recognition module. The video vehicle detection in this paper is divided into four steps: video image pre-processing, background modeling and updating, foreground detection and adhesion vehicle area segmentation. After the collected video sequence is subjected to mean-value down-sampling and gray-scale conversion preprocessing, the background model is built using a mixture of Gaussian models. A sliding window is set on the time axis, and the grayscale video frame image in the sliding window is median-filtered to output a background gradient map. By merging the results of the spatial background difference between the color space and the grayscale gradient, the foreground image detected by the vehicle is obtained. Finally, the background difference is obtained in the gradient domain to obtain the foreground image to achieve vehicle detection.

AN ALGORITHM FOR SPEED ESTIMATION AND SPEED VIOLATION DETECTION OF A VEHICLE USING COMPUTER VISION

Vehicle speed detection and violation detection is in automated way is very useful task for intelligent traffic surveillance system. Vehicle tracking in a precise way is critical task for the process of estimating the speed using only video data. This paper proposes an algorithm to estimate the speed and detecting the violation in precise way. The proposed approach gives the method which contains the optical flow algorithm with corner detection for tracking the vehicle. Proposed algorithm results the better tracking accuracy and better accuracy for speed detection. Also less computation complexity as optical flow is calculated for only corner points.

Infrared traffic image enhancement algorithm based on dark channel prior and gamma correction

The infrared traffic image acquired by the intelligent traffic surveillance equipment has low contrast, little hierarchical differences in perceptions of image and the blurred vision effect. Therefore, infrared traffic image enhancement, being an indispensable key step, is applied to nearly all infrared imaging based traffic engineering applications. In this paper, we propose an infrared traffic image enhancement algorithm that is based on dark channel prior and gamma correction. In existing research dark channel prior, known as a famous image dehazing method, here is used to do infrared image enhancement for the first time. Initially, in the proposed algorithm, the original degraded infrared traffic image is transformed with dark channel prior as the initial enhanced result. A further adjustment based on the gamma curve is needed because initial enhanced result has lower brightness. Comprehensive validation experiments reveal that the proposed algorithm outperforms the current state-of-the-art algorithms.

Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

Traffic surveillance and speed limit violation detection system

India has shown rapid economic development and this has forced the country to adopt smart traffic management and surveillance process. The intelligent traffic management and surveillance is the basic need for development of smart cities in India. The Intelligent traffic surveillance includes the de tection of moving vehicles, estimation of their speed and extracting its registration number. This paper proposes an efficient and novel approach for the detection of moving vehicles as well as estimation of their speeds for detection of any speed limit violation by using a single camera in daylight or properly illuminated environment. The proposed approach detects and tracks the vehicle passing through the surveillance area and maintains the record of vehicles position. The Region of Interest (ROI) of the approach is the dark shaded area formed under the vehicles road clearance area instead of the vehicles. The average detection accuracy achieved by proposed approach is 93.32%. This is on an average 7.54% higher than other existing approaches. The proposed approach uses cropping operation to minimize the scope of any false positive detection on both sides of road. The average tracking accuracy of the proposed approach is about 92.2%. The tracking accuracy achieved by the proposed approach is on an average 2.2% higher than the other leading approaches. The average speed estimation error in proposed approach is +2.2 km/h which is lower than other existing leading approaches. The proposed approach is also time and space efficient over the existing approaches.

Trajectory-based vehicle tracking at low frame rates

In smart cities, an intelligent traffic surveillance system plays a crucial role in reducing traffic jams and air pollution, thus improving the quality of life. An intelligent traffic surveillance should be able to detect and track multiple vehicles in real-time using only limited resources. Conventional tracking methods usually run at a high video-sampling rate, assuming that the same vehicles in successive frames are similar and move only slightly. However, in cost effective embedded surveillance systems (e.g., a distributed wireless network of smart cameras), video frame rates are typically low because of limited system resources. Therefore, conventional tracking methods perform poorly in embedded surveillance systems because of discontinuity of the moving vehicles in the captured recordings. In this study, we present a fast and light algorithm that is suitable for an embedded real-time visual surveillance system to detect effectively and track multiple moving vehicles whose appearance and/or position changes abruptly at a low frame rate. For effective tracking at low frame rates, we propose a new matching criterion based on greedy data association using appearance and position similarities between detections and trackers. To manage abrupt appearance changes, manifold learning is used to calculate appearance similarity. To manage abrupt changes in motion, the next probable centroid area of the tracker is predicted using trajectory information. The position similarity is then calculated based on the predicted next position and progress direction of the tracker. The proposed method demonstrates efficient tracking performance during rapid feature changes and is tested on an embedded platform (ARM with DSP-based system).

Improved multi-camera smart traffic surveillance system for resilient cities

In this paper, intelligent traffic surveillance system as an important part of a smart resilient city is reviewed. The smart traffic control system is very important in improving the life style by decreasing the traffic saturation and air pollution. Still, multi-camera vision is more helpful in implementing different automatic traffic surveillance systems. Apart from their superior features, existing multi-camera surveillance systems suffer from computational complexity and degraded accuracy. The main reason for these shortcomings arises from image processing errors. These errors depend on the image content and thus are not predictable. To overcome this shortage, three-dimensional (3D) optical techniques for improved fusing the multi-camera images and thus extracting 3D vehicle locations is proposed in this paper. In fact the proposed multi-camera visionary system is a combination of image processing based and geometrical optics based methods. The result of 3D image reconstruction through the proposed technique shows its dominance in providing the 3D image information.

An accurate and practical calibration method for roadside camera using two vanishing points

Calibrating roadside camera is essential and indispensable for intelligent traffic surveillance systems. Due to the characteristics of the traffic scenarios, the commonly used camera calibration methods based on calibration patterns are no longer suitable, since there are generally no calibration patterns (e.g., checkerboard) in traffic scenarios. In this paper, we propose an accurate and practical calibration method for roadside camera, where the vanishing point in the direction of traffic lane and the vertical vanishing point are employed that can be easily obtained from most traffic scenarios. By making full use of video information, the multiple observations of two vanishing points are available. Using these observations, we present a dynamic calibration method to correct camera parameters, where the least squares estimation is employed instead of closed-form computation. The experimental results on synthetic data show the accuracy and robustness to the noise of vanishing points; the experimental results on real traffic images demonstrate the effectiveness and practicability of the proposed calibration method.