Traffic data of multiple vehicle types are important for pavement design, traffic operations and traffic control. For the complex traffic in developing countries, no suitable tool is available for collecting such data. Considering the importance of these data and problems of traffic data collection, a new video-based traffic data collection system for multiple vehicle types is developed. This image processing-based system can track and classify vehicles under mixed traffic conditions. The type and speed of every passing vehicle are recognized. It is worth mentioning that this system takes the cross-lane vehicles into consideration and takes some measures to reduce the classification errors caused by vehicle occlusions. Finally, the flows and mean speeds of multiple vehicle types are output. This system was tested under four different weather conditions. The accuracy of vehicle counting was 97.4% and the error of vehicle classification was 8.3%. The correlation coefficient of speeds detected by this system and radar gun was 0.898 and the mean error of speed detection by this system was only 2.3 km/h. This indicates that this system is reliable for collecting traffic data of multiple vehicle types.

Video-Based Traffic Data Collection System for Multiple Vehicle Types

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a total of 70 research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the preferable way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Networks (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09899427.pdf

A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Abstract The road is the most commonly used means of transportation and serves as a country’s arteries, so it is extremely important to keep the roads in good condition. Potholes that happen to appear in the road must be repaired to keep the road in good condition. Spotting potholes on the road is difficult, especially in a country like India where roads stretch millions of kilometres across the country. Therefore, there is a need to automate the identification of potholes with high speed and real-time precision. YOLOX is an object detection algorithm and our main goal of this article is to train and analyse the YOLOX model for pothole detection. The YOLOX model is trained with a pothole dataset and the results obtained are analysed by calculating the accuracy, recall and size of the model which is then compared to other YOLO algorithms. The experimental results in this article show that the YOLOX-Nano model predicts potholes with higher accuracy compared to other models while having low computational costs. We were able to achieve an Average Precision (AP) value of 85.6% from training the model and the total size of the model is 7.22 MB. The pothole detection capabilities of the newly developed YOLOX algorithm have never been tested before and this paper is one of the first to detect potholes using the YOLOX object detection algorithm. The research conducted in this paper will help reduce costs and increase the speed of pothole identification and will be of great help in road maintenance. 

/pdf/enhanced-pothole-detection-system-using-yolox-algorithm-2zcs0xeb.pdf

Enhanced pothole detection system using YOLOX algorithm

Edge detection using adjusted Chebyshev polynomials on contrast-enhanced images by modified histogram equalization

Received Oct 17, 2022 Revised Dec 20, 2022 Accepted Feb 3, 2023 It is still a challenge for all authors to control an autonomous mobile robot in an unstructured environment. The purpose of this paper is to propose a new control method for mobile robots in unstructured environments using neuro fuzzy technique. The proposed algorithm reduces the processing time of the fuzzy logic controller (FLC) inference engine. The neural network (NN) will therefore select the optimum rule(s) directly from the inference engine. This means that all of the rules of the inference engine do not need to be processed. As a result, the inference engine process speed will decrease, and the fuzzy logic response will increase. An actual mobile robot with three distance sensors and one virtual orientation angle is used to test the proposed algorithm. Based on the results, the mobile robot is capable of avoiding all obstacles and reaching the target point accurately.

Bulletin of Electrical Engineering and Informatics

Indian pothole detection based on CNN and anchor-based deep learning method

The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.

http://ijai.iaescore.com/index.php/IJAI/article/download/20658/pdf

Vehicle detection and tracking for traffic management

Recent developments in Information and Communication Technology have facilitated a new concept of Smart City to the world. The Smart City concept is driven by technology intervention in every aspect of city life, including the most dynamic and unpredictable transport management. Intelligent transport management system (ITMS) is the most essential component of a Smart City ecosystem. The primary function is to ensure smooth and accident-free transport on the city roads. ITMS can prompt drivers of possible traffic jams; ITMS can be used to detect speed violations of vehicles. One of the primary inputs to ITMS is fed from closed circuit television (CCTV) installations on the roads. The main objective of the paper is to detect vehicles violating traffic rules especially over-speeding. The detection of over-speeding of a vehicle involves detection of vehicle, calculation and calibration of the distance traveled by the vehicle both on an image plane and real world. To calibrate the distance traveled by the vehicle, the geometric plane of the real world is projected onto the image plane. The projection onto the image plane helps in determining the actual distance traveled by the vehicle in the real world. After calibration of the distance traveled by the vehicle, speed calculation is performed. The accuracy of the algorithm to speed detection is 90.8%.

Image projection method for vehicle speed estimation model in video system

Corner based statistical modelling in vehicle detection under various condition for traffic surveillance

The intelligent traffic management system (ITS) is one of the active research areas. Vehicle detection is a major role in traffic analysis. In the paper, analysis of detecting vehicles is proposed based on the features posed by the vehicle. The foreground pixels from image are extracted by histogram based foreground segmentation. After segmenting, Hu-Moments and Eigen values features are extracted and normalized. The classifiers are trained with the extracted Hu-Moments and Eigen values. The experiments are conducted on different benchmark datasets, and results are analysed considering the overall classification accuracy. Results of the algorithm are satisfactory and acceptable in real time. 

/pdf/geometric-invariant-features-for-the-detection-and-analysis-7mw777fm.pdf

Pavana Baligar

Papers

Indian pothole detection based on CNN and anchor-based deep learning method

Vehicle detection and tracking for traffic management

Image projection method for vehicle speed estimation model in video system

Corner based statistical modelling in vehicle detection under various condition for traffic surveillance

Geometric invariant features for the detection and analysis of vehicle