Vehicle detection for intelligent traffic surveillance system

This paper introduces a time domain energy detector (ED) approach for vehicle detection using a single microphone. The detection of a deterministic, as well as the stochastic signal in the presence of white Gaussian noise (WGN), is a very challenging task. The ground vehicle generates a similar kind of signal with unknown information. Therefore, there is need of robust vehicle detection algorithm which must detect a vehicle with low false alarm. We proposed an algorithm based on energy detector and estimation of threshold value depending on noise's variance for moving vehicle detection using acoustics data. The energy distribution of the signal follows the chi-square distribution. These decision statistics reveal the vehicle presence information from the signal and detection probability of a true event. The algorithm is tested on the experimental data acquired from the field trials of the moving vehicle. The receiver operating characteristics are computed through Monte Carlo simulations for validation of the proposed algorithm. The results show that the algorithm is capable to detect the moving vehicle with low false alarm even at low signal to noise ratio.

In the field of intelligent transportation, accurate vehicle detection, tracking, and re-identification are essential tasks that enable real-time monitoring, congestion management, and safety improvements. To address these needs in high-traffic highway environments, this study proposes a multi stage traffic flow model combining deep learning and metric learning. The model leverages the Segment Anything Model for vehicle detection, utilizing language-prompting to automate segmentation, thereby reducing manual adjustments and improving adaptability across complex traffic scenarios. For vehicle tracking, the model employs the StrongSORT algorithm, integrated with mask-based tracking to enhance recognition coherence and maintain resilience against occlusions, especially in congested conditions. Additionally, the PP-OCR module accurately extracts timestamps to support interval speed measurement across multiple viewpoints, using vehicle re-identification results for precise multi-camera tracking. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art methods, achieving higher mean average precision and tracking accuracy in both high-density and challenging traffic conditions, highlighting its robustness and suitability for intelligent transportation systems.

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration.

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.

Advanced Driver Assistance Systems (ADAS) and Collision Avoidance Systems (CAS) are the primary modules of modern human-centric and autonomous driving applications, such as forward and rear-end collision warnings. To enhance the performance of ADAS and CAS systems in foggy weather, an Efficient and Cost-Effective Vehicle Detection and Tracking with Driver Assistance (ECE-VDTDA) system is proposed. The proposed ECE-VDTDA system comprises vehicle detection, tracking, and driver assistance modules. An optimized SimYOLO-V5s_WIOU vehicle detection algorithm is proposed, based on the SimSPPF module, the baseline You Only Look Once (YOLO) algorithm (YOLO-V5s), and the Wise Intersection Over Union (WIOU) localization loss function. State-of-the-art Deep-SORT, Strong-SORT, and optimized Deep-SORT algorithms are utilized for vehicle tracking. The vehicle detection and tracking performance of the ECE-VDTDA system is rigorously evaluated on DAWN, foggy driving, foggy cityscapes, BDD100K, web-collected, and self-collected foggy weather datasets. Optimized SimYOLO-V5s_WIOU algorithm outperformed on the foggy driving dataset with a 17.45% increase in mAP50, and foggy cityscapes dataset with a 0.32%, 1.05%, 1.58%, 2%, 0.54% increase in the multiclass mAP50, mAP50-95, F1 score, precision, and recall scores, respectively, compared to the baseline YOLO-V5s. Furthermore, the SimYOLO-V5s_WIOU algorithm also outperformed the state-of-the-art methods and enables Deep-SORT, Strong-SORT, and optimized Deep-SORT vehicle tracking algorithms to track vehicles with high confidence. The driver assistance module of the ECE-VDTDA system helps prevent imminent road collisions in foggy weather by estimating distance, speed, and time-to-collision and by issuing timely collision warnings. The experimental results demonstrate the robustness and computational efficiency of the proposed ECE-VDTDA system.

Vehicle detection in aerial images is an important and challenging task in the field of remote sensing. Recently, deep learning technologies have yielded superior performance for object detection in remote sensing images. However, the detection results of the existing methods are horizontal bounding boxes that ignore vehicle orientations, thereby having limited applicability in scenes with dense vehicles or clutter backgrounds. In this article, we propose a one-stage, anchor-free detection approach to detect arbitrarily oriented vehicles in high-resolution aerial images. The vehicle detection task is transformed into a multitask learning problem by directly predicting high-level vehicle features via a fully convolutional network. That is, a classification subtask is created to look for vehicle central points and three regression subtasks are created to predict vehicle orientations, scales, and offsets of vehicle central points. First, coarse and fine feature maps outputted from different stages of a residual network are concatenated together by a feature pyramid fusion strategy. Upon the concatenated features, four convolutional layers are attached in parallel to predict high-level vehicle features. During training, task uncertainty learned from the training data is used to weight loss function in the multitask learning setting. For inferencing, oriented bounding boxes are generated using the predicted vehicle features, and oriented nonmaximum suppression (NMS) postprocessing is used to reduce redundant results. Experiments on two public aerial image data sets have shown the effectiveness of the proposed approach.

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

Automatic vehicle detection from high-resolution remote sensing image is a challenging topic. While there have been some studies on this topic in recent years, a fast and robust approach is yet to be found, especially when facing the scenario of low color contrast. In this paper, a new vehicle detection approach is proposed. First, superpixel-based segmentation is used to identify potential vehicle regions to speed up the detection and improve the accuracy. Then, an image enhancement method is also proposed, which greatly improves the segmentation results. Support vector machine is used to classification with features extracted by HOG descriptor. According to the experiments, by combining with superpixel segmentation and the image enhancement, the speed of the vehicle detection is improved with approximately an order of magnitude. Also, in case of low contrast remote sensing images, the detection accuracy can be also greatly improved, with much less false positives and false negatives.

Nowadays, as people’s demands and lifestyles change the need for advancing the type of technology we use is high. Everything we used has been innovated to a better standard. Intelligent and autonomous vehicles are promising solutions to improve road safety, traffic problems, and passenger comfort in advanced driving assistant systems (ADAS). Such applications require advanced computer vision algorithms that require high-speed computers. In some cases, it is still a significant challenge to keep smart vehicles on the road right up to their destination, especially when driving at high speed. The first main task is robust navigation, often based on system vision, to obtain RGB road images for further processing. Depending on the position, speed, and direction of the vehicle, the second task is the dynamic control. This document presents precise and efficient road borders and an intelligent and autonomous vehicle detection algorithm for painted lines. It combines the Hough Transform and Canny edges detector to initialize the algorithm at every time necessary. Lastly, you only look once (YOLO), a new detection approach, is presented. Classifiers for the detection of objects are repurposed prior to work. We instead frame object detection into separate spatial boundaries and associated class probabilities as a regression problem.

With the rapid growth of the global economy, the global car ownership is also increasing year by year, which has caused a series of problems, the most prominent of which is traffic congestion and traffic accidents. In order to solve the traffic problem, all countries are actively studying the intelligent transportation system, and one of the important research contents of the intelligent transportation system is vehicle detection. Vehicle detection based on vision is to capture vehicle images in the driving environment through a camera, and then use computer vision recognition technology for vehicle detection and recognition. Although computer vision recognition technology has made great progress, how to improve the detection accuracy of the image to be detected is still an important content of visual recognition technology research. Intelligent vehicle visual robust detection and identification of methods of research to reduce the growing incidence of traffic accidents, improve the existing road traffic safety and transportation efficiency, alleviate the degree of driver fatigue problem are of great significance. This paper considers the intelligent vehicle environmental awareness of the key technology to the goal of robust detection and recognition based on machine vision problems for further research. The particle filter is used to extract the local energy of the image to realize the fast segmentation of the region of interest (ROI). In order to further verify the ROI, a measure learning method based on multi-core embedding is proposed, and the semantic classification of ROI is realized by integrating the color, shape and geometric features of ROI. Experimental results show that the algorithm can effectively eliminate false sexy ROI interest, and the algorithm is robust to complex background, illumination changes, perspective changes and other conditions.

Vehicle counting is a process to estimate traffic density on roads to assess the traffic conditions for intelligent transportation systems (ITS). Real-time traffic management systems have become popular recently due to the availability of high end cameras and technology. The present traffic management systems focus on speed detection, signal jumping, zebra crossing but not on traffic density estimation. Proposed video-based vehicle counting and tracking method using a video captured on CCTV and handheld mobile cameras. The system can be used in smart cities to create smart traffic light signals, in which duration of each signal depends on real time vehicle density in a particular lane of road. Vehicle counting is performed in two steps: the captured video is sent to You Only Look Once (YOLO) based deep learning framework to detect, count and classify the vehicles. Multi vehicular tracking is adopted using Deep SORTalgorithm to track the vehicles in video frames. Model was trained for six different classes, using Google Colaboratory. Datasets of vehicles specifically pertaining to Indian roads environment is considered for implementation. The performance of the model was analyzed, proposed model has tested and obtained an average counting accuracy of 86.56% while the average precision is 93.85%. The model can be implemented for ascertaining the traffic density on roads and this provides knowledge for infrastructural development to authorities. It can also be an integral part of smart city projects to develop intelligent and smart traffic surveillance system.

Automated vehicle detection is a research field in constant evolution due to the new technological advances and security requirements demanded by the current intelligent transportation systems. For these reasons, in this paper we present a vision-based vehicle detection and tracking pipeline, which is able to run on an iPhone in real time. An approach based on smartphone cameras supposes a versatile solution and an alternative to other expensive and complex sensors on the vehicle, such as LiDAR or other range-based methods. A multi-scale proposal and simple geometry consideration of the roads based on the vanishing point are combined to overcome the computational constraints. Our algorithm is tested on a publicly available road dataset, thus demonstrating its real applicability to ADAS or autonomous driving.

Transportation plays an important role in urban development However, the vehicle growth in Indonesia is not supported by the number of road. Due to this fact, traffic congestion is easily occurred, especially in big cities. Intelligent Transportation System (ITS) has huge contribution to decrease the traffic congestion. In ITS, vehicle detection is one of challenging issue for traffic surveillance. In this paper, adaptive selective background learning and hole filling algorithm are applied to improve the vehicle detection. The validity of the proposed method is tested by using three scenarios and two parameters. The scenarios are bad weather close range (BW-CR), normal weather close range (NW-CR) and normal weather wide range (NW-WR). While, the parameters are the time duration of stopped vehicle detection and the pixel accuracy. Then, the proposed method (Adaptive Selective Background Learning-Hole Filling algorithm) is compared by another previous vehicle detection method. Generally, the result shows that the proposed method yields a significant improvement in vehicle detection. ASBL-HF can detect the stopped and moved vehicle with free noises. Moreover, ASBL-HF has the best accuracy. The accuracy value is about 98.2%.

Vehicle detection and tracking plays a major role in military and civilian applications. The accurate detection of multiple vehicles in a complicated traffic environment is too difficult. This process is made more difficult, if there are occlusions between vehicles. The optimal feature selection of a vehicle is also a challenging task. These issues are focused in the existing work namely convolution neural network (CNN) based vehicle detection. However in this work, feature selection is not concentrated and with the lesser feature information classification accuracy is reduced considerably. This is focused and resolved in the proposed research by introducing the method namely Enhanced Convolution neural network with Support Vector Machine (ECNN-SVM) based vehicle detection. In this work initially feature extraction is done. Haar like features are used for this purpose. This has compact representation, capture information from multiple scales, encoded edge and structural information and can be efficient computation. Haar like feature pool is large scale in nature. Enhanced bat optimization is used to select features from this pool. This selection is done by combining sample’s class label and feature value. Confidence level between two thresholds of tracking rectangles is used for classification. Support vector machine (SVM) classifier is combined with local binary pattern to perform this classification. The interference areas between moving objects and vehicles are removed by Enhanced Convolutional Neural Network (ECNN) classifier. The efficiency of the proposed method is computed by using experimental and theoretical analysis.

Automotive and advanced driver assistance systems have attracted a great deal of attention lately. In these systems, effective and reliable vehicle detection is important because such systems can reduce the number of accidents and save human’ lives. This paper describes an approach to detecting a forward vehicle using a camera mounted on the moving vehicle. In this paper, we describe two methods to detect a vehicle on the road. First, by using the vehicle’s shadow, we can obtain the general location of the vehicular candidate. Second, we can identify the strong vertical edges at the left and right position of a vehicle. By combining the shadows and the edge, we can detect the vehicle’s location. But other regions may also be detected, such as car windows, reflections, and illumination by the sun. In order to remove these other factors, defined as noises, we need to use a filter. After using the filter, we can calculate the exact location of the vehicle. Additionally, by using connected component labeling, we can obtain coordinates and establish the vehicle’s location. Connected component labeling find all connected components in an image and assigns a unique label to all points in the same component. These methods are very useful for vehicle detection and the development of the driving assistance systems, and they can protect drivers’ safety from having an accident.