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A Novel Framework for vehicle detection and tracking in Night Ware Surveillance Systems

IEEE ACCESS 2024年 12卷 88075-88085页

作者： Almujally, Nouf Abdullah Qureshi, Asifa Mehmood Alazeb, Abdulwahab Rahman, Hameedur Sadiq, Touseef Alonazi, Mohammed Algarni, Asaad Jalal, Ahmad Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Air Univ Fac Comp Sci & AI Islamabad 44000 Pakistan Najran Univ Coll Comp Sci & Informat Syst Dept Comp Sci Najran 55461 Saudi Arabia Univ Agder Ctr Artificial Intelligence Res Dept Informat & Commun Technol N-4879 Grimstad Norway Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Informat Syst Al Kharj 16273 Saudi Arabia Northern Border Univ Fac Comp & Informat Technol Dept Comp Sci Rafha 91911 Saudi Arabia

In the field of traffic surveillance systems, where effective traffic management and safety are the primary concerns, vehicle detection and tracking play an important role. Low brightness, low contrast, and noise are issues with low-light environments that result from poor lighting or insufficient exposure. In this paper, we proposed a vehicle detection and tracking model based on the aerial image captured during nighttime. Before object detection, we performed fogging and image enhancement using MIRNet architecture. After pre-processing, YOLOv5 was used to locate each vehicle position in the image. Each detected vehicle was subjected to a Scale-Invariant Feature Transform (SIFT) feature extraction algorithm to assign a unique identifier to track multiple vehicles in the image frames. To get the best possible location of vehicles in the succeeding frames templates were extracted and template matching was performed. The proposed model achieves a precision score of 0.924 for detection and 0.861 for tracking with the Unmanned Aerial vehicle Benchmark Object detection and tracking (UAVDT) dataset, 0.904 for detection, and 0.833 for tracking with the Vision Meets Drone Single Object-tracking (VisDrone) dataset.

关键词： Feature extraction vehicle detection YOLO Computational modeling Lighting Brightness Deep learning Image analysis Surveillance Traffic control Defogging deep learning yolov5 feature fusion vehicle detection and tracking image normalization

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Evaluation on application of intelligent traffic image recognition system in vehicle detection and tracking

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INTERNATIONAL JOURNAL OF EMBEDDED SYSTEMS 2023年第5-6期16卷 375-384页

作者： Liu, Cheng Jiangsu Vocat Inst Commerce Coll Digital Business Nanjing 210000 Jiangsu Peoples R China

This paper studied from three aspects: the structure of vehicle detection system and the use of intelligent traffic image recognition system video information collection and analysis, the use of intelligent traffic image recognition system to design vehicle detection algorithms, and the use of intelligent traffic image recognition system to track the application of moving vehicles. Through experiments and research, this paper built a new vehicle detection and tracking system, and the satisfaction rate was 19% higher than that of the traditional vehicle detection and tracking system. Compared with the traditional vehicle detection and tracking system, the accuracy of the new vehicle detection and tracking system was increased by 0.28, and the definition was increased by 0.4. This can be in order to better serve people and solve traffic problems such as urban congestion. Therefore, the construction of intelligent transportation system is very important.

关键词： intelligent traffic imagery image recognition system vehicle detection and tracking video image processing intelligent transportation system ITS

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Real-time vehicle detection and tracking using 3D LiDAR

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ASIAN JOURNAL OF CONTROL 2022年第3期24卷 1459-1469页

作者： Wang, Heng Zhang, Xiaodong Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Sch Automat & Elect Engn Beijing 100083 Peoples R China Chaoyue Technol Co Ltd Jinan Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China

This paper studies the problem of vehicle detection and tracking for an autonomous vehicle using 3D Light detection and Ranging (LiDAR). In order to increase the accuracy of vehicle detection and tracking, a new clustering algorithm is proposed to obtain vehicle candidates from preprocessed point cloud data collected by the LiDAR. A classifier trained by the support vector machine (SVM) algorithm is used to detect vehicles from vehicle candidates. Kalman filter and global nearest neighbor (GNN) algorithm are used to track vehicles, and the accuracy of vehicle detection result is further improved by the aid of tracking results. The proposed method has been verified on a testing platform.

关键词： LiDAR point cloud SVM vehicle detection and tracking

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Night Time vehicle detection and tracking by Fusing vehicle Parts From Multiple Cameras

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第7期23卷 8136-8156页

作者： Zhang, Xinxiang Story, Brett Rajan, Dinesh Southern Methodist Univ Lyle Sch Engn Dept Elect & Comp Engn Dallas TX 75205 USA Southern Methodist Univ Lyle Sch Engn Dept Civil & Environm Engn Dallas TX 75205 USA

Night time vehicle detection and tracking using traditional visible light cameras is a challenging task due to the limited visibility. The current state-of-the-art systems treat the vehicles at night time as paired vehicle headlights or taillights, with no ability to determine the contour of the vehicle or its spatial occupancy. Therefore, this paper proposes the first night time framework that combines the vehicle headlights and taillights to localize the vehicle contours. This new framework includes a novel multi-camera vehicle representation that groups and reconstructs vehicle headlights and taillights following mutual geometric distances between different vehicle components. This novel vehicle contour representation successfully removes duplicated vehicle lights and also compensates for the missing vehicle lights in the detection process. Eventually, vehicle headlight alignment and contour adjustment are used to further refine the vehicle contours. The proposed multi-camera system considers typical four-wheel vehicles, e.g., cars and SUVs, in the monitoring and might not be able to handle large trucks, e.g., 18-wheelers. The experiments are conducted on night time traffic videos under various scenarios and the proposed system attains an average of 0.896 in Multiple Object tracking Accuracy (MOTA) and an average of 0.904 in Jaccard Coefficient (JC), which indicates 19.2% and 15.9% increases over the baseline system. The night time traffic datasets used in this paper are available at https://***/JustMeZXX/Intelligent-Night-TimeTraffic-Surveillance

关键词： Multi-camera night time vehicle detection and tracking vehicle contours vehicle representation four wheels

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Dual-Branch CNNs for vehicle detection and tracking on LiDAR Data

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2021年第11期22卷 6942-6953页

作者： Vaquero, Victor del Pino, Ivan Moreno-Noguer, Francesc Sola, Joan Sanfeliu, Alberto Andrade-Cetto, Juan CSIC UPC Inst Robot & Informat Ind Barcelona 08028 Spain Univ Alicante Automat Robot & Comp Vis Grp AUROVA Alicante 03690 Spain

We present a novel vehicle detection and tracking system that works solely on 3D LiDAR information. Our approach segments vehicles using a dual-view representation of the 3D LiDAR point cloud on two independently trained convolutional neural networks, one for each view. A bounding box growing algorithm is applied to the fused output of the networks to properly enclose the segmented vehicles. Bounding boxes are grown using a probabilistic method that takes into account also occluded areas. The final vehicle bounding boxes act as observations for a multi-hypothesis tracking system which allows to estimate the position and velocity of the observed vehicles. We thoroughly evaluate our system on the KITTI benchmarks both for detection and tracking separately and show that our dual-branch classifier consistently outperforms previous single-branch approaches, improving or directly competing to other state of the art LiDAR-based methods.

关键词： Three-dimensional displays Laser radar vehicle detection Sensors Task analysis Cameras Convolutional neural networks Deep convolutional neural network vehicle detection and tracking LiDAR point cloud

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Sensor Fusion-Based vehicle detection and tracking Using a Single Camera and Radar at a Traffic Intersection

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SENSORS 2023年第10期23卷 4888页

作者： Li, Shenglin Yoon, Hwan-Sik Univ Alabama Dept Mech Engn Tuscaloosa AL 35487 USA

Recent advancements in sensor technologies, in conjunction with signal processing and machine learning, have enabled real-time traffic control systems to adapt to varying traffic conditions. This paper introduces a new sensor fusion approach that combines data from a single camera and radar to achieve cost-effective and efficient vehicle detection and tracking. Initially, vehicles are independently detected and classified using the camera and radar. Then, the constant-velocity model within a Kalman filter is employed to predict vehicle locations, while the Hungarian algorithm is used to associate these predictions with sensor measurements. Finally, vehicle tracking is accomplished by merging kinematic information from predictions and measurements through the Kalman filter. A case study conducted at an intersection demonstrates the effectiveness of the proposed sensor fusion method for traffic detection and tracking, including performance comparisons with individual sensors.

关键词： sensor fusion roadside camera and radar traffic monitoring Kalman filter vehicle detection and tracking intelligent traffic system

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Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehicle detection and tracking in unstructured environment

Real-time 3D-Lidar, MMW Radar and GPS/IMU fusion based vehic...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Li, Ning Lu, Caixia Yu, Xuewei Liu, Xueyan Su, Bo China North Vehicle Res Inst Beijing 100072 Peoples R China

ISBN: (纸本)9781728190778

To solve the problem of unmanned ground vehicle leader-follower formation transportation in unstructured environment, we propose a novel target detection and tracking method based on multi-sensor fusion perception. Combined with 3D-Lidar, millimeter wave Radar and GPS/IMU, the proposed method can achieve stable target detection and continuous tracking of both static and dynamic vehicles. First, 3D-Lidar is used to detect the geometric model of the leader vehicle to complete the initialization of tracking target and it can also be assisted for target tracking. Then during the movement, the dynamic leader is mainly tracked through millimeter wave Radar as this sensor can keep tracking the same target with a constant index and effectively distinguish dynamic vehicle from other static obstacles according to relative speed estimation. In addition, by using GPS/IMU based integrated navigation, the movement trend of the leader can be derived according to the echo vehicle pose information and the relative position relationship. This is helpful to reduce the region of interest for target tracking and improve the real-time performance. In different unstructured environments, we perform the leader-follower formation transportation experiments for hundreds of kilometers. In rough terrain, the maximum tracking speed can still reach 40km/h and the maximum tracking distance can be up to 100 meters. Experiments show that the proposed method is suitable for vehicle target detection and tracking in unstructured environment. It has good robustness and high real-time performance with an average processing frame rate of 20Hz. The proposed method can be used for the formation transportation of unmanned ground vehicles to reduce labor costs.

关键词： vehicle detection and tracking unstructured environment multi-sensor fusion unmanned ground vehicle

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Efficient vehicle detection and tracking strategy in aerial videos by employing morphological operations and feature points motion analysis

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MULTIMEDIA TOOLS AND APPLICATIONS 2020年第35-36期79卷 26023-26043页

作者： Gomaa, Ahmed Abdelwahab, Moataz M. Abo-Zahhad, Mohammed Natl Res Inst Astron & Geophys NRIAG Helwan 11731 Egypt Egypt Japan Univ Sci & Technol Elect & Commun Engn Dept Alexandria 21934 Egypt Kyushu Univ Ctr Japan Egypt Cooperat Sci & Technol Nishi Ku Fukuoka 8190395 Japan Assiut Univ Elect & Elect Engn Dept Fac Engn Assiut 71511 Egypt

Real-time automatic detection and tracking of moving vehicles in videos acquired by airborne cameras is a challenging problem due to vehicle occlusion, camera movement, and high computational cost. This paper presents an efficient and robust real-time approach for automatic vehicle detection and tracking in aerial videos that employ both detections and tracking features to enhance the final decision. The use of Top-hat and Bottom-hat transformation aided by the morphological operation in the detection phase has been adopted. After detection, background regions are eliminated by motion feature points' analysis of the obtained object regions using a combined technique between KLT tracker and K-means clustering. Obtained object features are clustered into separate objects based on their motion characteristic. Finally, an efficient connecting algorithm is introduced to assign the vehicle labels with their corresponding cluster trajectories. The proposed method was tested on videos taken in different scenarios. The experimental results showed that the recall, precision, and tracking accuracy of the proposed method were about 95.1 %, 97.5%, and 95.2%, respectively. The method also achieves a fast processing speed. Thus, the proposed approach has superior overall performance compared to newly published approaches.

关键词： Morphological operations Aerial surveillance Remote sensing KLT tracker K-means clustering vehicle detection and tracking

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On-Road vehicle detection and tracking Using MMW Radar and Monovision Fusion

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第7期17卷 2075-2084页

作者： Wang, Xiao Xu, Linhai Sun, Hongbin Xin, Jingmin Zheng, Nanning Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China

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 systemis installed on Kuafu-II intelligent vehicle for the fourth and fifth autonomous vehicle competitions, which is called "Intelligent vehicle Future Challenge" in China.

关键词： Intelligent vehicle vehicle detection and tracking sensor fusion MMW radar monocular camera

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Likelihood-Field-Model-Based Dynamic vehicle detection and tracking for Self-Driving

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2016年第11期17卷 3142-3158页

作者： Chen, Tongtong Wang, Ruili Dai, Bin Liu, Daxue Song, Jinze Natl Univ Def Technol Coll Mechatron Engn & Automat Changsha 410073 Hunan Peoples R China Massey Univ Sch Engn & Adv Technol Auckland 0632 New Zealand Natl Univ Def Technol Unmanned Syst Inst Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Grad Sch Changsha 410073 Hunan Peoples R China

Dynamic vehicle detection and tracking is crucial for self-driving in urban environments. The main problem of the previous beam-model-based algorithms is that they cannot detect and track dynamic vehicles that are occluded by other objects. In this paper, we develop a novel dynamic vehicle detection and tracking algorithm to solve this problem for our autonomous land vehicle (ALV), which is equipped with a Velodyne LIDAR and a GPS-aid inertial navigation system. For detection, our improved two-dimensional virtual scan is presented to detect the potential dynamic vehicles with a scan differencing operation. Then, for each potential dynamic vehicle, a novel likelihood-field-based vehicle measurement model is proposed to weight its possible poses. Finally, our newly modified scaling series algorithm and the importance sampling technique are adopted to estimate the initial pose and the corresponding velocity for each vehicle, respectively. The scaling series algorithm coupled with a Bayesian filter (SSBF) was previously used to handle the tactile localization problem in static background scenes. For tracking dynamic vehicles, we improve the SSBF by adding the ego-motion compensation so that the improved algorithm is able to update the pose and velocity for each vehicle in dynamic background scenes. Both the quantitative and qualitative experimental results validate the performance of our dynamic vehicle detection and tracking algorithm on the KITTI datasets and the Velodyne data collected by our ALV in dynamic urban environments.

关键词： Autonomous land vehicle (ALV) Bayesian filter likelihood-field-based vehicle measurement model scaling series algorithm vehicle detection and tracking

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