In the last decades, visual target tracking has been one of the primary research interests of the Robotics research community. The recent advances in Deep Learning technologies have made the exploitation of visual tracking approaches effective and possible in a wide variety of applications, ranging from automotive to surveillance and human assistance. However, the majority of the existing works focus exclusively on passive visual tracking, i.e., tracking elements in sequences of images by assuming that no actions can be taken to adapt the camera position to the motion of the tracked entity. On the contrary, in this work, we address visual active tracking, in which the tracker has to actively search for and track a specified target. Current State-of-the-Art approaches use Deep Reinforcement Learning (DRL) techniques to address the problem in an end-to-end manner. However, two main problems arise: (i) most of the contributions focus only on discrete action spaces, and the ones that consider continuous control do not achieve the same level of performance; and (ii) if not properly tuned, DRL models can be challenging to train, resulting in considerably slow learning progress and poor final performance. To address these challenges, we propose a novel DRL-based visual active tracking system that provides continuous action policies. To accelerate training and improve the overall performance, we introduce additional objective functions and a Heuristic Trajectory Generator (HTG) to facilitate learning. Through extensive experimentation, we show that our method can reach and surpass other State-of-the-Art approaches performances, and demonstrate that, even if trained exclusively in simulation, it can successfully perform visual active tracking even in real scenarios.

在过去的几十年中，视觉目标跟踪一直是机器人技术研究界的主要研究兴趣之一。深度学习技术的最新发展已使视觉跟踪方法的开发在汽车，监视和人工协助等各种应用中有效且可行。但是，大多数现有作品仅专注于被动视觉跟踪，即通过假定无法采取任何措施使相机位置适应被跟踪实体的运动来跟踪图像序列中的元素。相反，在这项工作中，我们着眼于视觉活动跟踪，其中跟踪器必须主动搜索和跟踪指定目标。当前的最新方法使用深度强化学习（DRL）技术以端到端的方式解决问题。但是，出现了两个主要问题：（i）大多数贡献仅集中在离散的动作空间上，而那些考虑到连续控制的表现则无法达到相同的性能水平；（ii）如果调整不当，DRL模型可能难以训练，从而导致学习进度相当缓慢且最终表现不佳。为了解决这些挑战，我们提出了一种新颖的基于DRL的视觉主动跟踪系统，该系统可提供连续的行动策略。为了加快培训并提高整体绩效，我们引入了其他目标函数和启发式轨迹生成器（HTG）以促进学习。通过广泛的实验，我们证明了我们的方法可以达到并超越其他先进方法的性能，并证明，即使专门进行模拟训练，它也可以在真实场景中成功执行视觉主动跟踪。