As a major type of transportation equipments, bicycles, including electrical bicycles, are distributed almost everywhere in China. The accidents caused by bicycles have become a serious threat to the public safety. So bicycle detection is one major task of traffic video surveillance systems in China. In this paper, a method based on multi-feature and multi-frame fusion is presented for bicycle detection in low-resolution traffic videos. It first extracts some geometric features of objects from each frame image, then concatenate multiple features into a feature vector and use linear support vector machine (SVM) to learn a classifier, or put these features into a cascade classifier, to yield a preliminary detection result regarding whether an object is a bicycle. It further fuses these preliminary detection results from multiple frames to provide a more reliable detection decision, together with a confidence level of that decision. Experimental results show that this method based on multi-feature and multi-frame fusion can identify bicycles with high accuracy and low computational complexity. It is, therefore, applicable for real-time traffic video surveillance systems.
Wuhan University of Technology（武汉理工大学）；National Engineering Research Center for Water Transport Safety（国家水运安全工程技术研究中心）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
The automatic identification system (AIS) and video cameras have been widely exploited for vessel traffic surveillance in inland waterways. The AIS data could provide the vessel identity and dynamic information on vessel position and movements. In contrast, the video data could describe the visual appearances of moving vessels, but without knowing the information on identity, position and movements, etc. To further improve vessel traffic surveillance, it becomes necessary to fuse the AIS and video data to simultaneously capture the visual features, identity and dynamic information for the vess…
Beyond conventional vision: RGB-event fusion for robust object detection in dynamic traffic scenarios
School of Information Engineering, Chang’an University（信息工程学院，长安大学）；School of Vehicle Mobility & College of AI, Tsinghua University（车辆运动学院与人工智能学院，清华大学）
Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk crite…
DRAFE: Domain-Robust Asymmetric Fusion of Heterogeneous Detection Transformers for Cross-City Fine-Grained Traffic Object Detection
Iowa State University（爱荷华州立大学）；University of Arkansas（阿肯色大学）；Parsons Corporation（帕森斯公司）；Jacobs（雅各布斯公司）；University of South Florida（南佛罗里达大学）；Neel-Schaffer, Inc.（尼尔-沙弗公司）
针对跨城市细粒度交通目标检测中细粒度识别与域泛化难兼顾的问题，提出 DRAFE 模型，通过两阶段训练及多模块融合策略，在 AI City Challenge 2026 Track 6 任务中取得 0.4022 mAP 排名第六的成绩，性能优于初步集成模型。
OnSiteVRU: A High-Resolution Trajectory Dataset for High-Density Vulnerable Road Users
School of Traffic；Transportation Engineering, Changsha University of Science \& Technology, No. 960, 2nd section of Wanjiali Road, Changsha, Hunan Province, 410114, China；Key Laboratory of Road；Traffic Engineering of Ministry of Education \& School of Transportation Engineering, Tongji University. No.4800 Cao’an Road, Shanghai, 201804, China.；School of Automobile；Transportation, Xihua University Chengdu Sichuan, China
Detecting dangerous traffic agents in videos captured by vehicle-mounted dashboard cameras (dashcams) is essential to facilitate safe navigation in a complex environment. Accident-related videos are just a minor portion of the driving video big data, and the transient pre-accident processes are highly dynamic and complex. Besides, risky and non-risky traffic agents can be similar in their appearance. These make risky object localization in the driving video particularly challenging. To this end, this paper proposes an attention-guided multistream feature fusion network (AM-Net) to localize dan…
Contrastive Learning-Driven Traffic Sign Perception: Multi-Modal Fusion of Text and Vision
School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能系统工程学院）；Guangdong Provincial Key Laboratory of Intelligent Transportation System（广东省智能交通系统重点实验室）
YOLO-TS: Real-Time Traffic Sign Detection with Enhanced Accuracy Using Optimized Receptive Fields and Anchor-Free Fusion
Sun Yat-sen University（中山大学）；Wuhan University of Technology（武汉理工大学）；Southeast University（东南大学）；Hong Kong Baptist University（香港浸会大学）；City University of Hong Kong（香港城市大学）
New Jersey Institute of Technology（新泽西理工学院）；Innovative AI Technologies（创新人工智能技术公司）
Automatic detection of traffic accidents is an important emerging topic in traffic monitoring systems. Nowadays many urban intersections are equipped with surveillance cameras connected to traffic management systems. Therefore, computer vision techniques can be viable tools for automatic accident detection. This paper presents a new efficient framework for accident detection at intersections for traffic surveillance applications. The proposed framework consists of three hierarchical steps, including efficient and accurate object detection based on the state-of-the-art YOLOv4 method, object tra…
Fudan University（复旦大学）；China University of Petroleum（中国石油大学）
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate overfitting in fusing multimodal features and develop some new features such as fractal measure of road complexity in satellite images, taxi flows, POIs, and road width and connectivity in OpenStreetMap. The solution is more promising in performance than the baseline methods and the single-modality data based solutions. After visualization from a micro view…
School of Computing and Augmented Intelligence, Arizona State University（亚利桑那州立大学计算与增强智能学院）；Department of Computer Science and Physics, Rider University（莱德大学计算机科学与物理系）
Manipal Institute of Technology, Manipal Academy of Higher Education（马尼帕尔高等教育学院马尼帕尔理工学院）
In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic problems. As an application of ITS, vehicle re-identification has gained ample interest in the domain of computer vision and robotics. Convolutional neural network (CNN) based methods are developed to perform vehicle re-identification to address key challenges such as occlusion, illumination change, scale, etc. The advancement of transformers in computer vision has opened an opportunity to explore the re-identification…
National Yang Ming Chiao Tung University（阳明交通大学）；Taiwan Semiconductor Research Institute（台湾半导体研究院）
Memory bandwidth has become the real-time bottleneck of current deep learning accelerators (DLA), particularly for high definition (HD) object detection. Under resource constraints, this paper proposes a low memory traffic DLA chip with joint hardware and software optimization. To maximize hardware utilization under memory bandwidth, we morph and fuse the object detection model into a group fusion-ready model to reduce intermediate data access. This reduces the YOLOv2's feature memory traffic from 2.9 GB/s to 0.15 GB/s. To support group fusion, our previous DLA based hardware employes a unifie…
Institute for Transport Planning and Systems, ETH Zurich（苏黎世联邦理工学院交通规划与系统研究所）
Accurate estimation of the traffic state over a network is essential since it is the starting point for designing and implementing any traffic management strategy. Hence, traffic operators and users of a transportation network can make reliable decisions such as influence/change route or mode choice. However, the problem of traffic state estimation from various sensors within an urban environment is very complex for several different reasons, such as availability of sensors, different noise levels, different output quantities, sensor accuracy, heterogeneous data fusion, and many more. To provi…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；University of Tübingen（图宾根大学）
How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such as object detection and motion forecasting. However, for the actual driving task, the global context of the 3D scene is key, e.g. a change in traffic light state can affect the behavior of a vehicle geometrically distant from that traffic light. Geometry alone may therefore be insufficient for effectively fusing representations in end-to-end driving models. In this work, we demonstrate that imitation learning policies based o…
Abnormal driving behaviour is one of the leading cause of terrible traffic accidents endangering human life. Therefore, study on driving behaviour surveillance has become essential to traffic security and public management. In this paper, we conduct this promising research and employ a two stream CNN framework for video-based driving behaviour recognition, in which spatial stream CNN captures appearance information from still frames, whilst temporal stream CNN captures motion information with pre-computed optical flow displacement between a few adjacent video frames. We investigate different s…
TrafficLoc: Localizing Traffic Surveillance Cameras in 3D Scenes
Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；DeepScenario；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
The trajectory prediction is significant for the decision-making of autonomous driving vehicles. In this paper, we propose a model to predict the trajectories of target agents around an autonomous vehicle. The main idea of our method is considering the history trajectories of the target agent and the influence of surrounding agents on the target agent. To this end, we encode the target agent history trajectories as an attention mask and construct a social map to encode the interactive relationship between the target agent and its surrounding agents. Given a trajectory sequence, the LSTM networ…
Multi-scale Temporal Fusion Transformer for Incomplete Vehicle Trajectory Prediction
University of California, Los Angeles (UCLA)（加利福尼亚大学洛杉矶分校）；FHWA Center of Excellence on New Mobility and Automated Vehicles（联邦公路管理局新 mobility 与自动驾驶汽车卓越中心）
Friedrich-Alexander University (FAU) Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）；Technische Hochschule Ingolstadt（因戈尔施塔特应用技术大学）；Research Institute AImotion Bavaria（巴伐利亚AImotion研究所）
Robust environment perception for autonomous vehicles is a tremendous challenge, which makes a diverse sensor set with e.g. camera, lidar and radar crucial. In the process of understanding the recorded sensor data, 3D semantic segmentation plays an important role. Therefore, this work presents a pyramid-based deep fusion architecture for lidar and camera to improve 3D semantic segmentation of traffic scenes. Individual sensor backbones extract feature maps of camera images and lidar point clouds. A novel Pyramid Fusion Backbone fuses these feature maps at different scales and combines the mult…
