3D Gaussian Splatting has marked a significant milestone in novel view synthesis, particularly in the real-time level rendering speed. Despite its remarkable contributions, this technique encounters substantial challenges when reconstructing dynamic scenes due to motion blur, which leads to inaccurate point clouds and low-resolution vision performance. To address this issue, this paper proposes an event camera assisting 3D Gaussian Splatting generation, which provides high-accuracy vision cues for reliable motion description. Because moving objects attract more attention, the entire scene is rendered at different precision scales. Specifically, the proposed solution provides a detailed rendering of the space captured by the event camera and reduces its rendering accuracy for static backgrounds. Our comparative experiments demonstrate that the adoption of event camera imagery significantly improves the fidelity of the novel view synthesis, thereby enhancing the overall reconstruction quality in dynamic scenes.
Under low-light conditions, existing RGB frame-based fall detection methods suffer from a significant decline in accuracy. To address this challenge, this paper proposes an innovative fall detection approach that exclusively utilizes RGB frames while incorporating adaptive image enhancement to improve performance. The proposed method leverages the Deep Deterministic Policy Gradient (DDPG) algorithm to estimate illumination conditions in the captured frames. By learning illumination characteristics, the DDPG algorithm accurately predicts parameters for image enhancement. Advanced techniques are then applied to adjust brightness and contrast, producing high-quality visuals even in dim environments. The effectiveness of this approach is validated using the YOLOv5 object detection algorithm to detect falls in both the original low-light images and their enhanced counterparts. Experimental results show that the proposed method significantly outperforms baseline approaches in low-light settings while maintaining real-time performance and robustness, offering a promising solution for fall detection.
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