Multi-Vehicle Tracking Based on Monocular Camera in Driver View

Multi-vehicle tracking is used in advanced driver assistance systems to track obstacles, which is fundamental for high-level tasks. It requires real-time performance while dealing with object illumination variations and deformations. To this end, we propose a novel multi-vehicle tracking algorithm b...

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Bibliographic Details
Published inApplied sciences Vol. 12; no. 23; p. 12244
Main Authors Lyu, Pengfei, Wei, Minxiang, Wu, Yuwei
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.12.2022
Subjects
Accuracy
Algorithms
Cameras
data association
Kalman filter
Kalman filters
multi-vehicle tracking
Neural networks
object detection
Sensors
Tracking problem
Vehicles
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Summary:Multi-vehicle tracking is used in advanced driver assistance systems to track obstacles, which is fundamental for high-level tasks. It requires real-time performance while dealing with object illumination variations and deformations. To this end, we propose a novel multi-vehicle tracking algorithm based on a monocular camera in driver view. It follows the tracking-by-detection paradigm and integrates detection and appearance descriptors into a single network. The one-stage detection approach consists of a backbone, a modified BiFPN as a neck layer, and three prediction heads. The data association consists of a two-step matching strategy together with a Kalman filter. Experimental results demonstrate that the proposed approach outperforms state-of-the-art algorithms. It is also able to solve the tracking problem in driving scenarios while maintaining 16 FPS on the test dataset.
ISSN:2076-3417
2076-3417
DOI:10.3390/app122312244