Efficient One-stage Video Object Detection by Exploiting Temporal Consistency
<|reference_start|>Efficient One-stage Video Object Detection by Exploiting Temporal Consistency: Recently, one-stage detectors have achieved competitive accuracy and faster speed compared with traditional two-stage detectors on image data. However, in the field of video object detection (VOD), most existing VOD methods are still based on two-stage detectors. Moreover, directly adapting existing VOD methods to one-stage detectors introduces unaffordable computational costs. In this paper, we first analyse the computational bottlenecks of using one-stage detectors for VOD. Based on the analysis, we present a simple yet efficient framework to address the computational bottlenecks and achieve efficient one-stage VOD by exploiting the temporal consistency in video frames. Specifically, our method consists of a location-prior network to filter out background regions and a size-prior network to skip unnecessary computations on low-level feature maps for specific frames. We test our method on various modern one-stage detectors and conduct extensive experiments on the ImageNet VID dataset. Excellent experimental results demonstrate the superior effectiveness, efficiency, and compatibility of our method. The code is available at https://github.com/guanxiongsun/vfe.pytorch.<|reference_end|>
@article{sun2024efficient,
    title={Efficient One-stage Video Object Detection by Exploiting Temporal
  Consistency},
    author={Guanxiong Sun, Yang Hua, Guosheng Hu, Neil Robertson},
    journal={arXiv preprint arXiv:2402.09241},
    year={2024},
    doi={10.1007/978-3-031-19833-5_1},
    archivePrefix={arXiv},
    eprint={2402.09241},
    primaryClass={cs.CV}
}