| 1. |
- Ma, Xiaoliang, Docent, et al.
(författare)
-
METRIC : Toward a Drone-based Cyber-Physical Traffic Management System
- 2022
-
Ingår i: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 3324-3329
-
Konferensbidrag (refereegranskat)abstract
- Drone-based system has a big potential to be applied for traffic monitoring and other advanced applications in Intelligent Transport Systems (ITS). This paper introduces our latest efforts of digitalising road traffic by various types of sensing systems, among which visual detection by drones provides a promising technical solution. A platform, called METRIC, is under recent development to carry out real-time traffic measurement and prediction using drone-based data collection. The current system is designed as a cyber-physical system (CPS) with essential functions aiming for visual traffic detection and analysis, real-time traffic estimation and prediction as well as decision supports based on simulation. In addition to the computer vision functions developed in the earlier stage, this paper also presents the CPS system architecture and the current implementation of the drone front-end system and a simulation-based system being used for further drone operations.
|
|
| 2. |
- Ning, Mang, et al.
(författare)
-
SeeFar : Vehicle Speed Estimation and Flow Analysis from a Moving UAV
- 2022
-
Ingår i: Image Analysis and Processing, ICIAP 2022, PT III. - Cham : Springer Nature. ; , s. 278-289
-
Konferensbidrag (refereegranskat)abstract
- Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: the speed estimation and flow analysis components are integrated into a unified framework; our method of predicting car speed has the least constraints while maintaining a high accuracy; our flow analysor is direction-aware and outlier-aware. Specifically, we design the speed estimator only using the camera imaging geometry, where the transformation between world space and image space is completed by the variable Ground Sampling Distance. Besides, previous papers do not evaluate their speed estimators at scale due to the difficulty of obtaining the ground truth, we therefore propose a simple yet efficient approach to estimate the true speeds of vehicles via the prior size of the road signs. We evaluate SeeFar on our ten videos that contain 929 vehicle samples. Experiments on these sequences demonstrate the effectiveness of SeeFar by achieving 98.0% accuracy of speed estimation and 99.1% accuracy of traffic volume prediction, respectively.
|
|
| 3. |
- Ning, Mang, et al.
(författare)
-
YOLOv4-object : an Efficient Model and Method for Object Discovery
- 2021
-
Ingår i: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 31-36
-
Konferensbidrag (refereegranskat)abstract
- Object discovery refers to recognising all unknown objects in images, which is crucial for robotic systems to explore the unseen environment. Recently, object detection models based on deep learning have shown remarkable achievements in object classification and localisation. However, these models have difficulties handling the unseen environment because it is infeasible to exhaustively predefine all types of objects. In this paper, we propose the model YOLOv4-object to recognise all objects in images by modifying the output space of YOLOv4 and related image labels. Experiments on COCO dataset demonstrate the effectiveness of our method by achieving 67.97% recall (6.49% higher than vanilla YOLOv4). We point out that the incomplete labels (COCO only labels for 80 categories) hurt the learning process of object discovery and a higher recall can be achieved by our method if the dataset is fully labelled. Moreover, our approach is transferable, extensible, and compressible, showing broad application scenarios. Finally, we conduct extensive experiments to illustrate the factors that affect the object discovery performance of our model and some suggestions on practical implementations are elaborated.
|
|
