Abstract
Based on the DeepSORT algorithm, this study explores the application of visual tracking technology in intelligent human-computer interaction, especially in the field of gesture recognition and tracking. With the rapid development of artificial intelligence and deep learning technology, visual-based interaction has gradually replaced traditional input devices and become an important way for intelligent systems to interact with users. The DeepSORT algorithm can achieve accurate target tracking in dynamic environments by combining Kalman filters and deep learning feature extraction methods. It is especially suitable for complex scenes with multi-target tracking and fast movements. This study experimentally verifies the superior performance of DeepSORT in gesture recognition and tracking. It can accurately capture and track the user's gesture trajectory and is superior to traditional tracking methods in terms of real-time and accuracy. In addition, this study also combines gesture recognition experiments to evaluate the recognition ability and feedback response of the DeepSORT algorithm under different gestures (such as sliding, clicking, and zooming). The experimental results show that DeepSORT can not only effectively deal with target occlusion and motion blur but also can stably track in a multi-target environment, achieving a smooth user interaction experience. Finally, this paper looks forward to the future development direction of intelligent human-computer interaction systems based on visual tracking and proposes future research focuses such as algorithm optimization, data fusion, and multimodal interaction in order to promote a more intelligent and personalized interactive experience. Keywords-DeepSORT, visual tracking, gesture recognition, human-computer interaction
Abstract (translated)
基于DeepSORT算法，本研究探讨了视觉跟踪技术在智能人机交互中的应用，特别是在手势识别和跟踪领域。随着人工智能和深度学习技术的迅速发展，基于视觉的互动逐渐取代传统输入设备，并成为智能系统与用户交流的重要方式。DeepSORT算法结合卡尔曼滤波器和深度学习特征提取方法，在动态环境中能够实现精准的目标追踪，尤其适用于复杂场景中的多目标追踪及快速移动情况。本研究通过实验验证了DeepSORT在手势识别和跟踪方面的优越性能：可以准确捕捉并跟踪用户的动作轨迹，并且在实时性和准确性方面优于传统的追踪方法。 此外，该研究还结合手势识别试验来评估不同手势（如滑动、点击、缩放等）下DeepSORT算法的识别能力和反馈响应。实验结果表明，DeepSORT不仅能有效处理目标遮挡和运动模糊问题，还能在多目标环境中稳定跟踪，从而实现流畅的人机交互体验。 最后，本文展望了基于视觉追踪的智能人机交互系统未来的发展方向，并提出诸如算法优化、数据融合及多模态互动等未来研究重点，以促进更加智能化和个人化的交互体验。关键词包括：DeepSORT、视觉跟踪、手势识别、人机交互。
URL
https://arxiv.org/abs/2505.07110
