In many scenarios, where videos are transmitted through bandwidth-limited channels for subsequent semantic analytics, the choice of frame rates has to balance between bandwidth constraints and analytics performance. Faced with this practical challenge, this study focuses on enhancing object tracking at low frame rates and proposes a learning Interpolation for tracking framework. This framework embeds an implicit video frame interpolation sub-network, which is concatenated and jointly trained with another object tracking sub-network. Once a low frame-rate video is an input, it is first mapped into a high frame-rate latent video, based on which the tracker is learned. Novel strategies and loss functions are derived to ensure the effective end-to-end optimisation of the authors’ network. On several challenging benchmarks and settings, their method achieves a highly competitive tradeoff between frame rate and tracking accuracy. As is known, the implications of interpolation on semantic video analytics and tracking remain unexplored, and the authors expect their method to find many applications in mobile embedded vision, Internet of Things and edge computing.

中文翻译：

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端到端学习插值技术，用于低帧率视频中的目标跟踪

在许多情况下，通过带宽受限的通道传输视频以进行后续语义分析时，帧速率的选择必须在带宽限制和分析性能之间取得平衡。面对这一实际挑战，本研究着重于以低帧频增强对象跟踪，并提出了一种学习内插的跟踪框架。该框架嵌入了一个隐式视频帧插值子网络，该子网络与另一个对象跟踪子网络连接并共同训练。输入低帧频视频后，首先将其映射到高帧频潜像中，以此为基础学习跟踪器。派生出新颖的策略和损失函数，以确保作者网络的有效端到端优化。在一些具有挑战性的基准和设置下，他们的方法在帧速率和跟踪精度之间实现了高度竞争的权衡。众所周知，插值对语义视频分析和跟踪的影响尚待探索，作者希望他们的方法能在移动嵌入式视觉，物联网和边缘计算中找到许多应用。