2,052 research outputs found
Framework for Motorcycle Risk Assessment Using Onboard Panoramic Camera
Traditional safety analysis methods based on historical crash data and simulation models have limitations in capturing real-world driving scenarios. In this experiment, panoramic videos recorded from a motorcyclist’s helmet in Bangkok, Thailand, were narrated using an image-to-text model and then put into a Large Language Model (LLM) to identify potential hazards and assess crash risks. The framework can assess static and moving objects with the potential for early warning and incident analysis. However, the limitations of the existing image-to-text model cause its inability to handle panoramic images effectively
STRIDE: Street View-based Environmental Feature Detection and Pedestrian Collision Prediction
This paper introduces a novel benchmark to study the impact and relationship
of built environment elements on pedestrian collision prediction, intending to
enhance environmental awareness in autonomous driving systems to prevent
pedestrian injuries actively. We introduce a built environment detection task
in large-scale panoramic images and a detection-based pedestrian collision
frequency prediction task. We propose a baseline method that incorporates a
collision prediction module into a state-of-the-art detection model to tackle
both tasks simultaneously. Our experiments demonstrate a significant
correlation between object detection of built environment elements and
pedestrian collision frequency prediction. Our results are a stepping stone
towards understanding the interdependencies between built environment
conditions and pedestrian safety
高速ビジョンを用いたリアルタイムビデオモザイキングと安定化に関する研究
広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora
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Semantic localisation via globally unique instance segmentation
In this work we propose a novel approach to semantic localisation. Our work is motivated by the need for environment perception techniques which not only perform self-localisation within a map but also simultaneously recognise surrounding objects. Such capabilities are crucial for computer vision applications which interact with the environment: autonomous driving, augmented reality or robotics. In order to achieve this goal we propose a solution which consists of three key steps. Firstly, a database of panoramic RGB images and corresponding globally unique, per-pixel object instance labels is built for the desired environment where we typically consider objects from static categories such as "building" or "tree". Secondly, a semantic segmentation network capable of predicting more than 3000 labels is trained on the collected data. Finally, for a given panoramic query image, the corresponding instance label image predicted by the network is used for semantic matching within the database. The matching is performed in two stages: (i) a fast retrieval of a small subset of database images (~100) with highly overlapping instance label histograms, followed by (ii) an explicit approximate 3 DoF (yaw, pitch, roll) alignment of the selected subset of images and the query image. We evaluate our approach in challenging indoor and outdoor navigation scenarios, achieving better or similar performance when compared to state-of-the-art image retrieval-based localisation approaches using key-point matching and image
level embedding. Our contribution includes: (i) a description of a novel semantic localisation approach using globally unique instance segmentation, (ii) corresponding quantitative and qualitative analysis and (iii) a novel CamVid-360 dataset containing 986 labelled instances of buildings, trees, road signs and poles
Robust visual odometry using uncertainty models
In dense, urban environments, GPS by itself cannot be relied on to provide accurate positioning information. Signal reception issues (e.g. occlusion, multi-path effects) often prevent the GPS receiver from getting a positional lock, causing holes in the absolute positioning data. In order to keep assisting the driver, other sensors are required to track the vehicle motion during these periods of GPS disturbance. In this paper, we propose a novel method to use a single on-board consumer-grade camera to estimate the relative vehicle motion. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Preliminary testing shows good accuracy and reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance. The effects of inaccurate calibration are examined using artificial datasets, suggesting a self-calibrating system may be possible in future work
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