Moving vehicle detection and tracking using monocular vision

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

This paper presents a lightweight and efficient vehicle detection and tracking method for smart intersection environments using a low-cost, low-resolutionFMCW(Frequency Modulated Continuous Wave) LiDAR(Light Detecting And Ranging) sensor.We propose a framework that leverages Doppler-based velocity information fromFMCWLiDAR to facilitate point cloud accumulation. This leads to more effective background removal and robust multi-object tracking, even with low-resolution LiDAR data. The point cloud accumulation technique compensates for the sparsity of points representing vehicles by leveraging the radial velocity of detected points, resulting in denser and more accurate representations of vehicles. Background removal and lane segmentation are applied to effectively remove static objects and improve detection accuracy. The Kalman filter incorporates both position and velocity information of detected objects, enabling reliable tracking even when frame rates are low or objects move rapidly. The overall system is designed for real-time operation on edge devices, making it suitable for deployment in practical smart city scenarios. Experimental validation using real-world road data demonstrates that the proposed method significantly enhances detection and tracking performance compared to conventional approaches.

Increasing driving safety by virtue of advanced technology requires real-time and accurate detection of vehicles in far and close distance, which pose a threat to the host vehicle. This paper presents a real-time on-road vehicle detection algorithm based on monocular vision. First, auto-adapted threshold segmentation is proposed to extract shadow features. Then, a special mask is used for morphology computing to retain the features of vehicles in far and close distance. And, vanishing point constraint is applied for the fast verification of vehicles. Finally, the tracking of vehicles assists to stabilize the detection results. The experiments show that the average processing speed reaches 39 frames/ms, and the fast detection of vehicle under different weather conditions in the day time can also work accurately.

The precise vehicle detection, following, and speed estimation are the essential elements of intelligent transportation system and traffic analytics. Conventional systems tend to be based on manual calibration or multi-sensors, which restrict scalability and flexibility. The paper introduces a vision-based, fully automated real-time vehicle monitoring system that combines the YOLO v11 object detector, an Enhanced SORT tracker to provide very robust multi-object tracking and an automatic camera calibration system to self-estimate the pixel-to-meter conversion ratio. The suggested system does not require manual calibration and takes advantage of such dimensions as typical vehicles, confidence-weighted fusion, and statistical filtering to stabilize scale estimation. Enhanced SORT tracker uses a Kalman filter-based motion model, and IOU + LAP association to get the same object ID and extract the velocity. Experimental results of testing on several traffic video streams show a mean processing rate of 25-30 frames/s, error of speed estimation of +-2 km/h and convergent calibration within 45 seconds. Structured CSV and JSON analytics are also produced by the system to analysis additional traffic flow and anomalies. The findings are that the suggested solution is a cost-efficient, scalable, and precise solution to real-time traffic monitoring and vehicle behavior analytics.

Aiming at vehicle detection and tracking problems in video monitoring and controlling system, this paper mainly studies vehicle detection and tracking problems in conditions of high traffic density in daytime. This paper is distinguished by two key contributions. First, we develop an improvement — SEAP(Simple but Efficient After Process) which checks the detection results in an accurate way and is an after process of Adaboost [1] detector which used to detect car in every frame. Second, we propose a tracking algorithm named 4-states tracking algorithm based on Kalman[5] linear filter. Tracking results turn unsteady as traffic density grows higher because of much more false positives and false negatives appear. However, 4-states tracking algorithm can solve this problem in an easy way by introducing FSM (Finite State Machine) into tracking algorithm. Finally, we implement a real-time vehicle detection and tracking system with the upper methods. Experiments give good results in relative crowded Conditions.

Real-time vehicle detection using histograms of oriented gradients and AdaBoost classification

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.

Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

This paper presents Fade, a vehicle detection and tracking system featuring monocular color vision and radar data fusion. Its main originality resides in its low-level fusion system. At each step and for each target, the fusion system fuses the results of four different image processing algorithms and radar information by automatically combining 12 different features and generating many possible target position proposals. It generates a belief network organized in three layers: sources, position proposals, and correlation between proposals. An inference algorithm is then used to find out the actual position of the target, by deducing which observations are wrong. The overall system runs in real time and has been evaluated on a variety of scenarios recorded in a real car using /sup RT/Maps. Fade yields very good detection results in most cases.

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.

When the general preceding vehicle detection is carried for monocular vision, the accuracy of the detection is affected by the dynamic background, illumination and occlusion. This paper proposed a preceding vehicle detection method fusing monocular and binocular vision method. Firstly, the potential area of the preceding vehicle was established by using SSD (Single Shot Multibox Detection) algorithm, then, the binocular vision-based vehicle detection method for the potential area was used, its U-V disparity was calculated, and the accurate preceding vehicle position by the vehicle depth information in the image was obtained; finally, the missed detection and false detections in the monocular vision detector were removed with the U-V disparity detections obtained by binocular vision detector. And the final results combined with the type and position information were outputted. Thereby the accuracy of the preceding vehicle detection was improved. The test result showed that on the condition of KITTI date set the visual fusion method can effectively improve the accuracy of vehicle detection. And compared with SSD algorithm in monocular, the Detection Rate was increased by 1.46%, the Recall Rate was increased by 2.83%, the Missed Rate was reduced by 2.83% and the False Rate was reduced by 1.10% than the monocular SSD algorithm.

Vehicle detection and tracking have been promising application in traffic surveillance and vehicular network.However, the vision-based approach still remains a challenging task due to the problems of illumination variation, shadow and occlusion.In this paper, we propose a robust framework mainly concatenates on two aspects: adaptive vehicle detection with shadow removal, and vehicle tracking with occlusion handling.Firstly, for vehicle detection stage, an improved ViBe algorithm with ghost suppression is adopted to extract moving vehicle region.Then moving shadow is removed by integrating improved color with texture feature.Then, aiming to achieve the multi-vehicle tracking, we propose an enhanced histogram of the oriented gradient combined with HSV color space based on particle filter (ECHOGPF).Finally, we employ the occlusion detection and occlusion segmentation to refine our system, which are based on one-dimensional maximum entropy and the least square ellipse fitting.Experiments on popular datasets show that our proposed system has a good effectiveness, e.g., the accuracy is 95% on the vehicle tracking.

With the rapid development of Internet of Things (IoT) and UAV technology, for the whole IoT system of vehicle detection, the middle and high level of information transmission and server processing has made a breakthrough, so at the bottom of the real-time detection of the vehicle by the UAV is the key to the whole system. However, UAV vehicle detection faces the challenges of too many small targets in the image leading to low detection accuracy, limited hardware platform resources requiring control of model size, and real-time detection requiring high inference speed. Aiming at the above problems, we propose a lightweight model PrFu-YOLO based on YOLOv8 improvement, which achieves a good balance between the accuracy, inference speed, and model size. And it realizes real-time vehicle detection embedded in an UAV platform. To solve the problem of low vehicle detection accuracy, we design a new structure PrFuFPN based on adding a small target detection layer to achieve more advanced feature fusion. To address the limited resources of the platform and the problem of real-time vehicle detection, we add GhostConv to the structure and constantly try to adjust the parameters of the network. Finally, extensive experiments were conducted on the VisDrone2019 and CARPK data sets to fully evaluate the model. Compared to YOLOv8s on the VisDrone2019 test set, mAP50 was improved by 10.05%, mA95 by 14.54%, the number of parameters was reduced by 8.13%, and the model size was reduced by 12.5%, while the FPS of 67.13 fully met the needs of real-time detection.

A Forward vehicle detection and lane detection algorithms are essential components of many advanced driver assistance systems. Conventional vehicle detection algorithms exhibit certain problems, including high computational complexity and low detection accuracy. This paper proposes a monocular vision-based vehicle detection and tracking algorithm using ego-lane information to improve computational efficiency and robustness. Firstly, vehicle candidate regions were determined to define the region of interest (ROI), which reduces the computation time. Secondly, the detected ROI was classified by means of the adaptive boosting cascade classifier, which is based on Haar-like features, in order to detect the vehicles’ rear view. Thirdly, edge and rear-light histograms obtained from previously detected vehicle locations were employed to predict the area in which the vehicle is located. Experiments were conducted to evaluate the proposed algorithm under various weather and illumination conditions using iROADS [34], one of the most popular datasets. The results show that the proposed algorithm performed well in real time (computation time of 15 ms) and showed high reliability in various road conditions.

This research focuses on the development of a real-time vehicle detection and tracking system using OpenCV and deep learning models to address growing traffic management challenges. The project integrates the Gaussian Mixture-based Background Subtraction (MOG2), morphological operations, contour detection, and tracking methods like Kalman Filtering and Deep SORT. Additionally, object classification via YOLO and MobileNet-SSD significantly improves the accuracy of identifying and distinguishing vehicles. Evaluated on multiple real-world datasets under various environmental conditions, the system demonstrated strong performance with over 91% mAP, 92.5% precision, and robust multi-object tracking accuracy. This study serves as a scalable framework for intelligent traffic surveillance.