Vehicle tracking using fuzzy-based vehicle detection window with adaptive parameters

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 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.

Vision-based vehicle tracking on highway traffic using bounding-box features to extract statistical information

With the potential to increase road safety and provide economic benefits, intelligent vehicles have elicited a significant amount of interest from both academics and industry. A robust and reliable vehicle detection and tracking system is one of the key modules for intelligent vehicles to perceive the surrounding environment. The millimeter-wave radar and the monocular camera are two vehicular sensors commonly used for vehicle detection and tracking. Despite their advantages, the drawbacks of these two sensors make them insufficient when used separately. Thus, the fusion of these two sensors is considered as an efficient way to address the challenge. This paper presents a collaborative fusion approach to achieve the optimal balance between vehicle detection accuracy and computational efficiency. The proposed vehicle detection and tracking design is extensively evaluated with a real-world data set collected by the developed intelligent vehicle. Experimental results show that the proposed system can detect on-road vehicles with 92.36% detection rate and 0% false alarm rate, and it only takes ten frames (0.16 s) for the detection and tracking of each vehicle. This system is installed on Kuafu-II intelligent vehicle for the fourth and fifth autonomous vehicle competitions, which is called “Intelligent Vehicle Future Challenge” in China.

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.

The system for tracking and monitoring lost or stolen vehicle is challenging. This device is widely used to assess the vehicle's location using GPS technology. It can be used to track a vehicle or vehicle fleet and to obtain information about the vehicle's current location. There are various challenges for tracking and monitoring vehicles and finding lost vehicles due to the lack of proper real-time vehicle location and hence it is difficult to take necessary action in the immediate proper time after the vehicle has lost or stolen. In this paper a Cluster Based Feasible Time Interval for Vehicle Tracking (CFTVT) algorithm for measuring the minimum time interval for taking action after the vehicle has lost or stolen is proposed. This proposed model helps to imply any appropriate vehicle tracking algorithm in the exact proper time after the vehicle has been lost or stolen.

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.

In this paper, an efficient vehicle detection and tracking algorithm for detection incident in tunnel is proposed. The proposed algorithm consists of three steps. The first one is a step for background estimates, low computational complexity and memory consumption Running Gaussian Average (RGA) is used. The second step is vehicle detection step, Adaboost algorithm is applied to this step. In order to reduce false detection from a relatively remote location of the vehicles, local features according to height of vehicles are used to detect vehicles. If the local features of an object are more than the threshold value, the object is classified as a vehicle. The last step is a vehicle tracking step, the Kalman filter is applied to track moving objects. Through computer simulations, the proposed algorithm was found that useful to detect and track vehicles in the tunnel.

Lane vehicle detection is fundamental to vehicle driving systems and self-driving. The proposed concept is to employ the pixel difference in the intended lane line backdrop to isolate the lane and the road surface, and then, the curve fitting model is used to identify the lane in the image. A histogram on gradient, histogram graph, and binary spatial features are extracted from the vehicle and non-vehicle images. For vehicle detection, support vector machine classifier is employed to separate the vehicle and non-vehicle images using the extracted features. But many methods are constrained by light conditions and road circumstances, such as weak light, fog, rain, etc., which may result in invisible lane lines. Feature extraction is the lane images being picked using various filters. Our work focuses on a lane detection technique founded on the Sobel filter and curve fitting model for lane line tracking in different conditions. Preprocessing encompasses the mitigation of noise as well as getting the image ready for the subsequent procedure. To achieve this, HLS color space was performed which identifies the lane by adding pixel values. The main aim is to increase the accuracy and reduce the computation time compared to other existing methods.KeywordsSobel filterCurve fitting modelLane detectionVehicle detectionSliding windowSupport vector machine

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.

We propose a vehicle tracking algorithm that takes a new occlusion reasoning approach. We consider two different types of occlusions: explicit occlusion and implicit occlusion. We also propose a traffic flow extraction method with the velocity and trajectory of the moving vehicles. The proposed vehicle tracking system is composed of three parts: vehicle segmentation, vehicle tracking, and traffic parameter extraction. The vehicle segmentation part separates moving vehicles from their background. We employ the adaptive background approach, which does not update the background of moving objects. We also design a 2D token-based algorithm for vehicle tracking using Kalman filtering that has a modest amount of computational complexity. The traffic parameter extraction part estimates the traffic parameters, such as the vehicle count and the average speed. It also extracts the traffic flows. Finally, we have evaluated the proposed algorithm with some MPEG-7 test sequences.

Real-time detection and tracking of a moving car in sequence of image frames acquired by a static camera and lane line detection using an onboard camera have been elucidated in this paper. Vehicle tracking has been performed by exploiting modelling behaviour of Kalman filter, and lane line detection has been done using modified Hough transform. In case of car tracking, image acquisition, detection of vehicle and locating its centroid are done using adaptive background subtraction technique. Parameter estimation of car’s position is further performed using Kalman filter. In case of lane detection, pre-processing of image, edge detection and masking results’ detection of broken lane lines are done. Hough transform, for detecting curvatures by exploiting the duality between points on a curve and curve parameters, is applied to join all broken parts and to get solid lane lines. The entire system has been implemented on a ‘VEEROBOT’ robotic car in laboratory environment. Real-time image has been captured using a ‘Logitech’ web camera. All relevant processing has been carried out by ‘Raspberry pi 3’. All the analyses of experimental results are elaborately presented in this paper.

Aiming at the problems of low detection and tracking accuracy, poor robustness and inaccurate vehicle flow statistics of multi-vehicle types in surveillance video, an artificial intelligence-based vehicle detection and tracking method was proposed. This method reconstructed the feature extraction network of the target detection model YOLOv5s to enhance the extraction of important features of the target and improve the detection accuracy of the detector. Firstly, Swin Transformer module is introduced into the Backbone network to replace part of C3 modules in the original algorithm, so as to enhance the global modeling ability of the model, better capture the context feature information, and expand the model's receptive field. Then, by comparing different attention mechanisms, NAM attention is selected to access the Neck network to enhance the cross-dimensional interaction between the channel and spatial dimensions of information, reduce information loss and enhance the performance of the network. Finally, the feature extraction network part of the DeepSort algorithm was optimized and retrained on the vehicle rerecognition data set to make it more suitable for vehicle tracking. The experimental results show that the accuracy of vehicle detection by the improved YOLOv5s is increased by 1.93 percentage points compared with the original algorithm. Combined with the DeepSort algorithm, the accuracy of vehicle flow statistics under different lighting conditions such as daytime, evening and night can reach 97.2%, 97.5% and 80.4% respectively.

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.

Tracking vehicles is an important and challenging issue in video-based intelligent transportation systems and has been broadly investigated in the past. This paper presents a robust and real-time method for tracking vehicles and the proposed algorithm includes two stages: vehicle detection, vehicle tracking. Vehicle detection is a key step and the concept of tracking vehicle is built upon the vehicle-segmentation method. According to the segmented vehicle shape, we propose a three-step prediction method based on the Kalman filter to track each vehicle. The proposed method has been tested on a number of monocular traffic-image sequences and the experimental results show that the algorithm is robust and real-time. The correct rate of vehicle tracking is higher than 85 percent, independent of environmental conditions.