Tracklet-refined multi-camera tracking based on balanced cross-domain re-identification for vehicles
Journal
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Pages
3978-3987
Date Issued
2021
Author(s)
Abstract
Mutli-camera vehicle tracking and re-identification (re-ID) have gradually gained attention due to their applications in the intelligent transportation system. However, these problems are fundamentally challenging. Specifically, for vehicle tracking, we observe that the results generated from single camera tracking algorithm usually recognize tracklets with same identity as different vehicles when the tracklets are occluded. Hence, we propose a Tracklet Re-connection technique to refine tracking results with pre-defined zone areas and GPS information. The proposed method can efficiently filter invalid tracklet pairs and reconnect the split tracklets into complete ones, which is important for the afterwards multi-target multi-camera tracking. As for re-ID, we also find that when a large-scale auxiliary dataset is used to assist the learning of main dataset for better model capability and generalization, there is a performance drop caused by data imbalance when the full auxiliary dataset is applied. To tackle this problem, we introduce Balanced Cross-Domain Learning to avoid the overemphasis on larger auxiliary dataset by a newly introduced training data sampler and loss function. The extensive experiments validate the empirical effectiveness of our proposed components. ? 2021 IEEE.
Subjects
Cameras
Intelligent systems
Intelligent vehicle highway systems
Large dataset
Cross-domain
Generalisation
Intelligent transportation systems
Large-scales
Multi-camera tracking
Multi-targets
Re identifications
Single camera tracking
Tracking algorithm
Tracklets
Vehicles
SDGs
Type
conference paper