Real-time Vehicle Detection and Tracking Algorithm for Forward Vehicle Collision Warning

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.

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.

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.

This paper aims at real-time vehicle detection and tracking. It presents a novel comprehensive algorithm by multi-cues fusion in sequence images based on monocular vision. Firstly, the vertical symmetry of vehicle rear view is utilised to extract the Region of Interest (ROI) so as to narrow the search area. And then, the sign of underneath shadow is employed to generate hypothetical positions on which potential vehicles maybe present. Following, both image intensity and figure information are used to verify the vertical symmetry of the potential vehicle candidates. Meanwhile, Mean Shift, based on the object features’ model of combine colour Histogram and Orientation Histogram (HOG), is employed to fast search the potential objects. More important, both detection and tracking are under an interactive mechanism which can dramatically improve detection efficiency. Experimental results show this approach can achieve 96.34% accurate rate and run on an average

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.

A real-time multi-modal people detection and tracking algorithm based on RGB-D information is presented in this paper. The color image and the depth image is calibrated and aligned firstly with each other. At texture level a SVM classifier based on HOG features is used to detect and identify human body in color images, while at depth level an Adaboost classifier with Haar-like features is used to detect human head in depth images respectively. With the space relationship between human body and head, a combined multi-modal cascade detector is then constructed, which can greatly improve the detection accuracy. For tracking tasks, the KCF(Kernelized Correlation Filter) algorithm is adopted to realize the real-time people tracking, in which color and depth features are both considered and used in template matching to guarantee the robustness of the tracking algorithm. The proposed algorithm is robust to object occlusion and tracking lost caused by fast movement or cross walking. Extensive experiments are carried out on a wheeled mobile robot mounted with a Kinect2 sensor, and the result shows the feasibility and effectiveness of the method.

An algorithm for real-time detection, localization and tracking of periodic signals, which appear as few-pixel blobs in video image sequences and have a characteristic binary pattern in the temporal domain, was developed, implemented and tested. All stages of the algorithm are described and discussed: (1) initial estimation and continuous re-estimation of global motion; (2) local 'signal augmentation' (the crucial step for detecting small-sized signals in image sequences); (3) temporal domain -- followed by spatial domain -- background estimation and subtraction; (4) binarization; (5) preliminary temporal domain pattern matching and subsequent pixel tracking; (6) signal detection and localization by post- processing and clustering, applied to the set obtained in (5); (7) temporal and spatial tracking of the located signals. The algorithm was tested by processing simulated as well as real image sequences. The results are discussed. The algorithm enables for efficient detection of visible signals, provides for reasonable detection even of invisible signals and yields an acceptable false alarm probability. It has also proved to be insensitive to local motions in a video film, to camera motion, to intensity changes and to any weak flickering of background. Strong flickering of background can, however, decrease the probability of detection.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this new era of intelligence and automation, it is important to develop intelligent software to analyse traffic data and detect abnormal activities occurring in the public. Information from GPS, Surveillance cameras, traffic management systems etc will be helpful for the researchers to develop such algorithms. In this research work, we propose a method to detect traffic accidents and used a deep convolutional neural network (D-CNN) and Centroid based vehicle tracking algorithm for vehicle detection. Overlapping bounding boxes and speed of the vehicle are considered for collision detection. The vehicle is tracked using a centroid tracking algorithm to find acceleration, speed and trajectory values of each vehicle in the continuous frames. The trajectory and angle change after the collision can be used to classify the accidents. The result shows a detection accuracy of 99% in such a way outperforms the other latest methods. The results from the proposed method can be used in several accident reconstruction softwares like PC crash, ARPro etc.

In this paper, we propose a real-time lane detection and tracking algorithm using a simple filter and Kalman filter to develop a Lane Departure Warning System(LDWS) that can be implemented in an embedded system. Our LDWS was realized on the I.MX6Q board, mounted on a test vehicle, and traveled about 1,000km to derive the experimental results. Experimental results show that our LDWS operates at 97% detection rate in the day time and 95% in the night time. The average processing time of the our LDWS is about 15frame per seconds.

The real-time fault detection and diagnosis algorithm of a liquid rocket engine is the basis of online reconfiguration of guidance and the control system of a launch vehicle, which is directly related to the success or failure of space mission. Based on previous related works, this paper carries out comparative experimental studies of relevant intelligent algorithm models for real-time fault detection engineering application requirements of a liquid hydrogen–oxygen rocket engine. Firstly, the working state and detection parameters’ selection of a hydrogen–oxygen engine are analyzed, and the proposed three real-time intelligent fault detection algorithm model design methods are elaborated again. Fault detection calculation and analysis are carried out through normal test data and fault test data. The comparative analysis results of real-time intelligent fault detection algorithm models is presented from three dimensions: detection time, fault detection, and stability and consistency. Finally, based on a correlation analysis, a comprehensive intelligent fault diagnosis model design framework is given to further solve the requirements of real-time fault detection and diagnosis engineering development of a liquid rocket engine, a complex piece of equipment.

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.

Aiming at the defects and shortages of traditional moving vehicles detection algorithms, by the analysis and comparison of the existing detection algorithms, we propose an algorithm that combined with frames with symmetric difference and background difference to detect moving vehicle in this paper. First, two different difference images by using frames with symmetric difference and background difference are gained respectively and two binary images can be gained by the appropriate threshold, then the contour of moving vehicles can be extracted by applying OR operation in the two binary images. Finally, the precise moving vehicles will be gained by mathematic morphological methods. In this paper we use Harris operator, Feature Points such as edges and corners are extracted, followed by block-matching to track the Feature Points in successive viedo frames. Many vehicles can be tracked at the same time automatically since the information is obtained from video sequences. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.2613

针对图像序列中运动目标检测、跟踪的难点问题，提出了一种实时运动目标检测与跟踪算法。该算法基于自适应背景建模，获取运动目标背景模型和前景图像，从而实现运动目标检测；通过建立运动目标的位置、大小、形状以及颜色分布模型，构造运动目标全局匹配函数，结合目标活力特征，实现多运动目标连续匹配和跟踪。实验结果表明，相对于传统的运动目标跟踪方法，本文方法明显减少了运算时间，增强了环境适应性，实现了复杂场景下运动目标的准确检测和稳定跟踪，对非刚性目标的形变、旋转具有较强的鲁棒性。In this paper, we propose a real-time mobile target detection and tracking algorithm for challenges of mobile target detection and tracking in sequential images. This algorithm based on the adaptive background modeling obtains background model and front-view images of mobile targets, which is the way to achieve target detection. Continuous matching and tracking of multiple mobile targets are realized through constructing position, size, shape and color distribution of the mobile targets, defining a global matching function for those targets, and associating their vitality characteristics. It is demonstrated by experiments that the algorithm presented in this paper, compared to the traditional methods of mobile target tracking, significantly reduces the computation time, improves adaptive feature to environments, achieves accurate detection and robust tracking of mobile targets in complex environments, and shows strong robustness to deformation and rotation of non-rigid targets.

Target detection and tracking is a key technology in intelligent monitoring system. However, it is still difficult to design a robust, accurate and real-time target detection and tracking algorithm. Taking target Tracking as the research focus, this paper focuses on one of the current mainstream algorithms, namely, TLD (tracking-learning-detection) target Tracking algorithm. Based on the theory of TLD target tracking and corner detection, a new target tracking method was proposed to solve the existing problems. A classifier is built online to track and learn the target in real time, and a target contour detector is added in the system to complement the random forest detector to solve the problem of tracking failure. Experimental results show that the proposed algorithm can effectively overcome the influence of fast change or depth occlusion, and improve the tracking efficiency of moving targets.

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.