Intelligent Traffic Surveillance System Research Articles

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.

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.

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.

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%.

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.

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.

Vehicle detection and recognition for intelligent traffic surveillance system

Vehicle detection and type recognition based on static images is highly practical and directly applicable for various operations in a traffic surveillance system. This paper will introduce the proc...

Probabilistic neural networks based moving vehicles extraction algorithm for intelligent traffic surveillance systems

Automated vehicle detection 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 or unstable bandwidth, especially in regard to wireless systems. This often hinders the detection of moving vehicles in variable bit-rate video streams. This paper presents a novel approach for vehicle detection based on probabilistic neural networks through artificial neural networks, which can accurately detect moving vehicles not only in high bit-rate video streams but also in low bit-rate video streams. The overall results of detection accuracy analyses demonstrate that the proposed approach has a substantially higher degree of both qualitative and quantitative efficacy than other state-of-the-art methods. For instance, the proposed method achieved Similarity and F1 accuracy rates that were up to 61.75% and 69.38% higher than the other compared methods, respectively.