JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection
<|reference_start|>JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object Detection: Event-based moving object detection is a challenging task, where static background and moving object are mixed together. Typically, existing methods mainly align the background events to the same spatial coordinate system via motion compensation to distinguish the moving object. However, they neglect the potential spatial tailing effect of moving object events caused by excessive motion, which may affect the structure integrity of the extracted moving object. We discover that the moving object has a complete columnar structure in the point cloud composed of motion-compensated events along the timestamp. Motivated by this, we propose a novel joint spatio-temporal reasoning method for event-based moving object detection. Specifically, we first compensate the motion of background events using inertial measurement unit. In spatial reasoning stage, we project the compensated events into the same image coordinate, discretize the timestamp of events to obtain a time image that can reflect the motion confidence, and further segment the moving object through adaptive threshold on the time image. In temporal reasoning stage, we construct the events into a point cloud along timestamp, and use RANSAC algorithm to extract the columnar shape in the cloud for peeling off the background. Finally, we fuse the results from the two reasoning stages to extract the final moving object region. This joint spatio-temporal reasoning framework can effectively detect the moving object from motion confidence and geometric structure. Moreover, we conduct extensive experiments on various datasets to verify that the proposed method can improve the moving object detection accuracy by 13\%.<|reference_end|>
@article{zhou2024jstr:,
    title={JSTR: Joint Spatio-Temporal Reasoning for Event-based Moving Object
  Detection},
    author={Hanyu Zhou, Zhiwei Shi, Hao Dong, Shihan Peng, Yi Chang, Luxin Yan},
    journal={arXiv preprint arXiv:2403.07436},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07436},
    primaryClass={cs.CV}
}