CyclingNet: Detecting cycling near misses from video streams in complex
urban scenes with deep learning
- URL: http://arxiv.org/abs/2102.00565v1
- Date: Sun, 31 Jan 2021 23:59:28 GMT
- Title: CyclingNet: Detecting cycling near misses from video streams in complex
urban scenes with deep learning
- Authors: Mohamed R. Ibrahim, James Haworth, Nicola Christie and Tao Cheng
- Abstract summary: CyclingNet is a deep computer vision model based on convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks.
After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets.
The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities.
- Score: 1.462434043267217
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Cycling is a promising sustainable mode for commuting and leisure in cities,
however, the fear of getting hit or fall reduces its wide expansion as a
commuting mode. In this paper, we introduce a novel method called CyclingNet
for detecting cycling near misses from video streams generated by a mounted
frontal camera on a bike regardless of the camera position, the conditions of
the built, the visual conditions and without any restrictions on the riding
behaviour. CyclingNet is a deep computer vision model based on convolutional
structure embedded with self-attention bidirectional long-short term memory
(LSTM) blocks that aim to understand near misses from both sequential images of
scenes and their optical flows. The model is trained on scenes of both safe
rides and near misses. After 42 hours of training on a single GPU, the model
shows high accuracy on the training, testing and validation sets. The model is
intended to be used for generating information that can draw significant
conclusions regarding cycling behaviour in cities and elsewhere, which could
help planners and policy-makers to better understand the requirement of safety
measures when designing infrastructure or drawing policies. As for future work,
the model can be pipelined with other state-of-the-art classifiers and object
detectors simultaneously to understand the causality of near misses based on
factors related to interactions of road-users, the built and the natural
environments.
Related papers
- Scoring Cycling Environments Perceived Safety using Pairwise Image
Comparisons [0.9299655616863538]
This study presents a novel approach to identifying how the perception of cycling safety can be analyzed and understood.
We repeatedly show respondents two road environments and ask them to select the one they perceive as safer for cycling.
We compare several methods capable of rating cycling environments from pairwise comparisons and classify cycling environments perceived as safe or unsafe.
arXiv Detail & Related papers (2023-07-25T10:31:45Z) - A Benchmark for Cycling Close Pass Near Miss Event Detection from Video
Streams [35.17510169229505]
We introduce a novel benchmark, called Cyc-CP, towards cycling close pass near miss event detection from video streams.
We propose two benchmark models based on deep learning techniques for these two problems.
Our models can achieve 88.13% and 84.60% accuracy on the real-world dataset.
arXiv Detail & Related papers (2023-04-24T07:30:01Z) - OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping [84.65114565766596]
We present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure.
OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes.
We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.
arXiv Detail & Related papers (2023-04-20T16:31:22Z) - Bent & Broken Bicycles: Leveraging synthetic data for damaged object
re-identification [59.80753896200009]
We propose a novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations or missing parts from subtle intra-class variations.
We leverage the power of computer-generated imagery to create, in a semi-automatic fashion, high-quality synthetic images of the same bike before and after a damage occurs.
As a baseline for this task, we propose TransReI3D, a multi-task, transformer-based deep network unifying damage detection.
arXiv Detail & Related papers (2023-04-16T20:23:58Z) - Monocular Cyclist Detection with Convolutional Neural Networks [0.0]
This study aims to reduce the number of vehicle-cyclist collisions, which are often caused by poor driver attention to blind spots.
We designed a state-of-the-art real-time monocular cyclist detection that can detect cyclists with object detection convolutional neural networks.
We conclude that this cyclist detection device can accurately and quickly detect cyclists and has the potential to improve cyclist safety significantly.
arXiv Detail & Related papers (2023-01-16T13:54:13Z) - Street-View Image Generation from a Bird's-Eye View Layout [95.36869800896335]
Bird's-Eye View (BEV) Perception has received increasing attention in recent years.
Data-driven simulation for autonomous driving has been a focal point of recent research.
We propose BEVGen, a conditional generative model that synthesizes realistic and spatially consistent surrounding images.
arXiv Detail & Related papers (2023-01-11T18:39:34Z) - CycleSense: Detecting Near Miss Incidents in Bicycle Traffic from Mobile
Motion Sensors [3.5127092215732176]
In cities worldwide, cars cause health and traffic problems which could be partly mitigated through an increased modal share of bicycles.
Many people, however, avoid cycling due to a lack of perceived safety.
For city planners, addressing this is hard as they lack insights into where cyclists feel safe and where they do not.
arXiv Detail & Related papers (2022-04-21T21:43:23Z) - Improving short-term bike sharing demand forecast through an irregular
convolutional neural network [16.688608586485316]
The study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast.
The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London.
The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods.
arXiv Detail & Related papers (2022-02-09T10:21:45Z) - Structured Bird's-Eye-View Traffic Scene Understanding from Onboard
Images [128.881857704338]
We study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image.
We show that the method can be extended to detect dynamic objects on the BEV plane.
We validate our approach against powerful baselines and show that our network achieves superior performance.
arXiv Detail & Related papers (2021-10-05T12:40:33Z) - Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories.
In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene.
We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
arXiv Detail & Related papers (2021-06-22T15:40:21Z) - SceneGen: Learning to Generate Realistic Traffic Scenes [92.98412203941912]
We present SceneGen, a neural autoregressive model of traffic scenes that eschews the need for rules and distributions.
We demonstrate SceneGen's ability to faithfully model distributions of real traffic scenes.
arXiv Detail & Related papers (2021-01-16T22:51:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.