Multi-feature fusion and memory-based mobile robot target tracking system

IF 1.5 Q3 AUTOMATION & CONTROL SYSTEMS IET Cybersystems and Robotics Pub Date : 2024-07-17 DOI:10.1049/csy2.12119
Hanqing Sun, Shijie Zhang, Qingle Quan
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Abstract

In crowded settings, mobile robots face challenges like target disappearance and occlusion, impacting tracking performance. Despite existing optimisations, tracking in complex environments remains insufficient. To address this issue, the authors propose a tailored visual navigation tracking system for crowded scenes. For target disappearance, an autonomous navigation strategy based on target coordinates, utilising a path memory bank for intelligent search and re-tracking is introduced. This significantly enhances tracking success. To handle target occlusion, the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced sensitivity. Servo control technology ensures robust target tracking by fully utilising appearance information and motion characteristics, even in occluded scenarios. Comprehensive testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments, affirming algorithm robustness.

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基于多特征融合和记忆的移动机器人目标跟踪系统
在拥挤的环境中,移动机器人会面临目标消失和遮挡等挑战,从而影响跟踪性能。尽管已有优化措施,但在复杂环境中的跟踪性能仍然不足。为解决这一问题,作者提出了一种针对拥挤场景的定制视觉导航跟踪系统。针对目标消失的情况,引入了基于目标坐标的自主导航策略,利用路径记忆库进行智能搜索和重新跟踪。这大大提高了跟踪的成功率。为了处理目标遮挡问题,系统依靠目标检测网络和特征记忆库提取的外观特征来提高灵敏度。伺服控制技术充分利用了外观信息和运动特征,即使在目标遮挡的情况下也能确保目标跟踪的稳定性。在 OTB100 数据集上进行的全面测试验证了该系统在各种拥挤环境中应对目标跟踪挑战的有效性,肯定了算法的鲁棒性。
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来源期刊
IET Cybersystems and Robotics
IET Cybersystems and Robotics Computer Science-Information Systems
CiteScore
3.70
自引率
0.00%
发文量
31
审稿时长
34 weeks
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