Amodal recognition is the ability of the system to detect occluded objects. Most SOTA Visual Recognition systems lack the ability to perform amodal recognition. Few studies have achieved amodal recognition through passive prediction or embodied recognition approaches. However, these approaches suffer from challenges in real-world applications, such as dynamic obstacles. We propose SeekNet, an improved optimization method for amodal recognition through embodied visual recognition. Additionally, we implement SeekNet for social robots, where there are multiple interactions with crowded pedestrians. We also demonstrate the benefits of our algorithm on occluded human detection and tracking over other baselines. Additionally, we set up a multi-robot environment with SeekNet to identify and track visual disease markers for airborne disease in crowded areas. We conduct our experiments in a simulated indoor environment and show that our method enhances the overall accuracy of the amodal recognition task and achieves the largest improvement in detection accuracy over time in comparison to the baseline approaches.
翻译:现代识别是该系统探测隐蔽物体的能力。大多数SOTA视觉识别系统缺乏进行现代识别的能力。很少有研究通过被动预测或体现的识别方法实现了现代识别。然而,这些方法在现实世界应用中遇到挑战,例如动态障碍。我们提议SearNet,这是通过体现的视觉识别实现现代识别的更优化方法。此外,我们为社会机器人实施SearNet,那里与拥挤的行人有多重互动。我们还展示了我们在隐蔽的人类检测和跟踪其他基线方面的算法的好处。此外,我们与Searnet建立了多机器人环境,以识别和跟踪拥挤地区的空气传播疾病视觉疾病标记。我们在模拟室内环境中进行实验,并表明我们的方法提高了模式识别任务的总体准确性,并比基线方法在时间上提高了探测准确性。</s>