A Semi-Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos
| Autor: | Prithviraj Dhar, Pirazh Khorramshahi, Amit Kumar, Jun-Cheng Chen, Wei-An Lin, Rama Chellappa |
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| Rok vydání: | 2018 |
| Předmět: |
Monocular
Computer science business.industry Pipeline (computing) ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology 010501 environmental sciences Tracking (particle physics) 01 natural sciences Task (computing) Transformation (function) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Affine transformation Artificial intelligence business 0105 earth and related environmental sciences |
| Zdroj: | CVPR Workshops |
| DOI: | 10.1109/cvprw.2018.00026 |
| Popis: | In this work, we present a novel approach for vehicle speed estimation from monocular videos. The pipeline consists of modules for multi-object detection, robust tracking, and speed estimation. The tracking algorithm has the capability for jointly tracking individual vehicles and estimating velocities in the image domain. However, since camera parameters are often unavailable and extensive variations are present in the scenes, transforming measurements in the image domain to real world is challenging. We propose a simple two-stage algorithm to approximate the transformation. Images are first rectified to restore affine properties, then the scaling factor is compensated for each scene. We show the effectiveness of the proposed method with extensive experiments on the traffic speed analysis dataset in the NVIDIA AI City challenge. We achieve a detection rate of 1.0 in vehicle detection and tracking, and Root Mean Square Error of 9.54 (mph) for the task of vehicle speed estimation in unconstrained traffic videos. |
| Databáze: | OpenAIRE |
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