Spatio-Temporal Context Modeling for Road Obstacle Detection
- URL: http://arxiv.org/abs/2301.07921v1
- Date: Thu, 19 Jan 2023 07:06:35 GMT
- Title: Spatio-Temporal Context Modeling for Road Obstacle Detection
- Authors: Xiuen Wu, Tao Wang, Lingyu Liang, Zuoyong Li, Fum Yew Ching
- Abstract summary: A data-driven context-temporal model of the driving scene is constructed with the layouts of the training data.
Obstacles are detected via state-of-the-art object detection algorithms, and the results are combined with the generated scene.
- Score: 12.464149169670735
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Road obstacle detection is an important problem for vehicle driving safety.
In this paper, we aim to obtain robust road obstacle detection based on
spatio-temporal context modeling. Firstly, a data-driven spatial context model
of the driving scene is constructed with the layouts of the training data.
Then, obstacles in the input image are detected via the state-of-the-art object
detection algorithms, and the results are combined with the generated scene
layout. In addition, to further improve the performance and robustness,
temporal information in the image sequence is taken into consideration, and the
optical flow is obtained in the vicinity of the detected objects to track the
obstacles across neighboring frames. Qualitative and quantitative experiments
were conducted on the Small Obstacle Detection (SOD) dataset and the Lost and
Found dataset. The results indicate that our method with spatio-temporal
context modeling is superior to existing methods for road obstacle detection.
Related papers
- Annotation-Free Curb Detection Leveraging Altitude Difference Image [9.799565515089617]
Road curbs are essential for ensuring the safety of autonomous vehicles.
Current methods for detecting curbs rely on camera imagery or LiDAR point clouds.
This work proposes an annotation-free curb detection method leveraging Altitude Difference Image (ADI)
arXiv Detail & Related papers (2024-09-30T10:29:41Z) - OOSTraj: Out-of-Sight Trajectory Prediction With Vision-Positioning Denoising [49.86409475232849]
Trajectory prediction is fundamental in computer vision and autonomous driving.
Existing approaches in this field often assume precise and complete observational data.
We present a novel method for out-of-sight trajectory prediction that leverages a vision-positioning technique.
arXiv Detail & Related papers (2024-04-02T18:30:29Z) - Perspective Aware Road Obstacle Detection [104.57322421897769]
We show that road obstacle detection techniques ignore the fact that, in practice, the apparent size of the obstacles decreases as their distance to the vehicle increases.
We leverage this by computing a scale map encoding the apparent size of a hypothetical object at every image location.
We then leverage this perspective map to generate training data by injecting onto the road synthetic objects whose size corresponds to the perspective foreshortening.
arXiv Detail & Related papers (2022-10-04T17:48:42Z) - Road Rutting Detection using Deep Learning on Images [0.0]
Road rutting is a severe road distress that can cause premature failure of road incurring early and costly maintenance costs.
This paper proposes a novel road rutting dataset comprising of 949 images and provides both object level and pixel level annotations.
Object detection models and semantic segmentation models were deployed to detect road rutting on the proposed dataset.
arXiv Detail & Related papers (2022-09-28T16:53:05Z) - Real-Time Accident Detection in Traffic Surveillance Using Deep Learning [0.8808993671472349]
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.
The robustness of the proposed framework is evaluated using video sequences collected from YouTube with diverse illumination conditions.
arXiv Detail & Related papers (2022-08-12T19:07:20Z) - Neural Motion Fields: Encoding Grasp Trajectories as Implicit Value
Functions [65.84090965167535]
We present Neural Motion Fields, a novel object representation which encodes both object point clouds and the relative task trajectories as an implicit value function parameterized by a neural network.
This object-centric representation models a continuous distribution over the SE(3) space and allows us to perform grasping reactively by leveraging sampling-based MPC to optimize this value function.
arXiv Detail & Related papers (2022-06-29T18:47:05Z) - SoDA: Multi-Object Tracking with Soft Data Association [75.39833486073597]
Multi-object tracking (MOT) is a prerequisite for a safe deployment of self-driving cars.
We propose a novel approach to MOT that uses attention to compute track embeddings that encode dependencies between observed objects.
arXiv Detail & Related papers (2020-08-18T03:40:25Z) - Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for
Online Collision Avoidance [95.86944752753564]
We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties.
Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates.
The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
arXiv Detail & Related papers (2020-07-28T07:34:30Z) - Tracking Road Users using Constraint Programming [79.32806233778511]
We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem.
Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
arXiv Detail & Related papers (2020-03-10T00:04:32Z) - Unsupervised Pixel-level Road Defect Detection via Adversarial
Image-to-Frequency Transform [8.644679871804872]
We propose an unsupervised approach to detecting road defects, using Adversarial Image-to-Frequency Transform (AIFT)
AIFT adopts the unsupervised manner and adversarial learning in deriving the defect detection model, so AIFT does not need annotations for road pavement defects.
arXiv Detail & Related papers (2020-01-30T04:50:00Z)
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.