Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation

Video Panoptic Segmentation (VPS) requires generating consistent panoptic segmentation and tracking identities to all pixels across video frames. Existing methods are mainly based on the trained instance embedding to maintain consistent panoptic segmentation. However, they inevitably struggle to cope with the challenges of small objects, similar appearance but inconsistent identities, occlusion, and strong instance contour deformations. To address these problems, we present HybridTracker, a lightweight and joint tracking model attempting to eliminate the limitations of the single tracker. HybridTracker performs pixel tracker and instance tracker in parallel to obtain the association matrices, which are fused into a matching matrix. In the instance tracker, we design a differentiable matching layer, ensuring the stability of inter-frame matching. In the pixel tracker, we compute the dice coefficient of the same instance of different frames given the estimated optical flow, forming the Intersection Over Union (IoU) matrix. We additionally propose mutual check and temporal consistency constraints during inference to settle the occlusion and contour deformation challenges. Extensive experiments demonstrate that HybridTracker outperforms state-of-the-art methods on Cityscapes-VPS and VIPER datasets.