Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs

• URL: http://arxiv.org/abs/2303.01991v2
• Date: Sun, 2 Apr 2023 17:25:52 GMT
• Title: Unified Perception: Efficient Depth-Aware Video Panoptic Segmentation with Minimal Annotation Costs
• Authors: Kurt Stolle and Gijs Dubbelman
• Abstract summary: We present a new approach titled Unified Perception that achieves state-of-the-art performance without requiring video-based training. Our method employs a simple two-stage cascaded tracking algorithm that (re)uses object embeddings computed in an image-based network.
• Score: 2.7920304852537536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Depth-aware video panoptic segmentation is a promising approach to camera based scene understanding. However, the current state-of-the-art methods require costly video annotations and use a complex training pipeline compared to their image-based equivalents. In this paper, we present a new approach titled Unified Perception that achieves state-of-the-art performance without requiring video-based training. Our method employs a simple two-stage cascaded tracking algorithm that (re)uses object embeddings computed in an image-based network. Experimental results on the Cityscapes-DVPS dataset demonstrate that our method achieves an overall DVPQ of 57.1, surpassing state-of-the-art methods. Furthermore, we show that our tracking strategies are effective for long-term object association on KITTI-STEP, achieving an STQ of 59.1 which exceeded the performance of state-of-the-art methods that employ the same backbone network. Code is available at: https://tue-mps.github.io/unipercept

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