Tracklet-Refined Multi-Camera Tracking Based on Balanced Cross-Domain Re-Identification for Vehicles
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 Reconnection technique to refine tracking results with predefined 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.