Spatio-Temporal Learnable Proposals for End-to-End Video Object Detection

• URL: http://arxiv.org/abs/2210.02368v2
• Date: Fri, 7 Oct 2022 14:07:38 GMT
• Title: Spatio-Temporal Learnable Proposals for End-to-End Video Object Detection
• Authors: Khurram Azeem Hashmi, Didier Stricker, Muhammamd Zeshan Afzal
• Abstract summary: We present SparseVOD, a novel video object detection pipeline that employs Sparse R-CNN to exploit temporal information. Our method significantly improves the single-frame Sparse RCNN by 8%-9% in mAP.
• Score: 12.650574326251023
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents the novel idea of generating object proposals by leveraging temporal information for video object detection. The feature aggregation in modern region-based video object detectors heavily relies on learned proposals generated from a single-frame RPN. This imminently introduces additional components like NMS and produces unreliable proposals on low-quality frames. To tackle these restrictions, we present SparseVOD, a novel video object detection pipeline that employs Sparse R-CNN to exploit temporal information. In particular, we introduce two modules in the dynamic head of Sparse R-CNN. First, the Temporal Feature Extraction module based on the Temporal RoI Align operation is added to extract the RoI proposal features. Second, motivated by sequence-level semantic aggregation, we incorporate the attention-guided Semantic Proposal Feature Aggregation module to enhance object feature representation before detection. The proposed SparseVOD effectively alleviates the overhead of complicated post-processing methods and makes the overall pipeline end-to-end trainable. Extensive experiments show that our method significantly improves the single-frame Sparse RCNN by 8%-9% in mAP. Furthermore, besides achieving state-of-the-art 80.3% mAP on the ImageNet VID dataset with ResNet-50 backbone, our SparseVOD outperforms existing proposal-based methods by a significant margin on increasing IoU thresholds (IoU > 0.5).

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