Efficient Vision-based Vehicle Speed Estimation

• URL: http://arxiv.org/abs/2505.01203v1
• Date: Fri, 02 May 2025 11:48:11 GMT
• Title: Efficient Vision-based Vehicle Speed Estimation
• Authors: Andrej Macko, Lukáš Gajdošech, Viktor Kocur,
• Abstract summary: We introduce several improvements to enhance real-time performance.<n>We evaluate our method in several variants on the BrnoCompSpeed dataset.<n>Our best performing model beats previous state-of-the-art in terms of median vehicle speed estimation error.
• Score: 1.6385815610837167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents a computationally efficient method for vehicle speed estimation from traffic camera footage. Building upon previous work that utilizes 3D bounding boxes derived from 2D detections and vanishing point geometry, we introduce several improvements to enhance real-time performance. We evaluate our method in several variants on the BrnoCompSpeed dataset in terms of vehicle detection and speed estimation accuracy. Our extensive evaluation across various hardware platforms, including edge devices, demonstrates significant gains in frames per second (FPS) compared to the prior state-of-the-art, while maintaining comparable or improved speed estimation accuracy. We analyze the trade-off between accuracy and computational cost, showing that smaller models utilizing post-training quantization offer the best balance for real-world deployment. Our best performing model beats previous state-of-the-art in terms of median vehicle speed estimation error (0.58 km/h vs. 0.60 km/h), detection precision (91.02% vs 87.08%) and recall (91.14% vs. 83.32%) while also being 5.5 times faster.

Related papers

• TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion [54.46664104437454]
  We propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion.<n>Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed.<n>Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%.
  arXiv  Detail & Related papers  (2025-04-16T05:25:04Z)
• KAN-RCBEVDepth: A multi-modal fusion algorithm in object detection for autonomous driving [2.382388777981433]
  This paper introduces the KAN-RCBEVDepth method to enhance 3D object detection in autonomous driving. Our unique Bird's Eye View-based approach significantly improves detection accuracy and efficiency. The code will be released in urlhttps://www.laitiamo.com/laitiamo/RCBEVDepth-KAN.
  arXiv  Detail & Related papers  (2024-08-04T16:54:49Z)
• StreamYOLO: Real-time Object Detection for Streaming Perception [84.2559631820007]
  We endow the models with the capacity of predicting the future, significantly improving the results for streaming perception. We consider multiple velocities driving scene and propose Velocity-awared streaming AP (VsAP) to jointly evaluate the accuracy. Our simple method achieves the state-of-the-art performance on Argoverse-HD dataset and improves the sAP and VsAP by 4.7% and 8.2% respectively.
  arXiv  Detail & Related papers  (2022-07-21T12:03:02Z)
• An approach to improving sound-based vehicle speed estimation [0.0]
  We consider improving the performance of a recently proposed sound-based vehicle speed estimation method. The method is tested on the VS10 dataset, which contains 304 audio-video recordings of ten different vehicles.
  arXiv  Detail & Related papers  (2022-04-08T12:58:35Z)
• Real Time Monocular Vehicle Velocity Estimation using Synthetic Data [78.85123603488664]
  We look at the problem of estimating the velocity of road vehicles from a camera mounted on a moving car. We propose a two-step approach where first an off-the-shelf tracker is used to extract vehicle bounding boxes and then a small neural network is used to regress the vehicle velocity.
  arXiv  Detail & Related papers  (2021-09-16T13:10:27Z)
• VSAC: Efficient and Accurate Estimator for H and F [68.65610177368617]
  VSAC is a RANSAC-type robust estimator with a number of novelties. It is significantly faster than all its predecessors and runs on average in 1-2 ms, on a CPU. It is two orders of magnitude faster and yet as precise as MAGSAC++, the currently most accurate estimator of two-view geometry.
  arXiv  Detail & Related papers  (2021-06-18T17:04:57Z)
• A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes [5.735035463793008]
  We present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane. We use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. We then use the estimated speeds to calibrate the parameters of a driver behavior model for the vehicles in the area of study.
  arXiv  Detail & Related papers  (2021-06-18T06:15:53Z)
• ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search [94.90294600817215]
  We propose a novel neural architecture search (NAS) method, termed ViPNAS, to search networks in both spatial and temporal levels for fast online video pose estimation. In the spatial level, we carefully design the search space with five different dimensions including network depth, width, kernel size, group number, and attentions. In the temporal level, we search from a series of temporal feature fusions to optimize the total accuracy and speed across multiple video frames.
  arXiv  Detail & Related papers  (2021-05-21T06:36:40Z)
• End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera [81.66569124029313]
  We propose a camera-based inter-vehicle distance and relative velocity estimation method based on end-to-end training of a deep neural network. The key novelty of our method is the integration of multiple visual clues provided by any two time-consecutive monocular frames. We also propose a vehicle-centric sampling mechanism to alleviate the effect of perspective distortion in the motion field.
  arXiv  Detail & Related papers  (2020-06-07T08:18:31Z)
• Detection of 3D Bounding Boxes of Vehicles Using Perspective Transformation for Accurate Speed Measurement [3.8073142980733]
  We present an improved version of our algorithm for detection of 3D bounding boxes of vehicles captured by traffic surveillance cameras. Our algorithm utilizes the known geometry of vanishing points in the surveilled scene to construct a perspective transformation. Compared to other published state-of-the-art fully automatic results our algorithm reduces the mean absolute speed measurement error by 32% (1.10 km/h to 0.75 km/h) and the absolute median error by 40% (0.97 km/h to 0.58 km/h)
  arXiv  Detail & Related papers  (2020-03-29T21:01:25Z)

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.