论文标题
实时暹罗多对象跟踪器，提出了增强的建议
Real-Time Siamese Multiple Object Tracker with Enhanced Proposals
论文作者
论文摘要
在实时视频中保持多个对象的身份是一项艰巨的任务，因为在每个帧上运行检测器并不总是可行的。因此，通常采用运动估计系统，这要么与目标数量不佳，要么产生具有有限语义信息的功能。为了解决上述问题，并允许实时跟踪数十个任意对象，我们提出了暹罗。 Siammotion包括一种新颖的提案引擎，该引擎通过注意机制和由惯性模块喂养并由特征金字塔网络提供动力的利益区域提取器产生质量特征。最后，提取的张量输入了一个比较头，该比较头有效地匹配了示例和搜索区域对，从而通过成对的深度区域建议网络和多对象惩罚模块生成质量预测。 Siammotion已在五个公共基准测试中得到了验证，并在当前的最新追踪器上取得了领先的表现。代码可用：https：//github.com/lorenzovaquero/siammotion
Maintaining the identity of multiple objects in real-time video is a challenging task, as it is not always feasible to run a detector on every frame. Thus, motion estimation systems are often employed, which either do not scale well with the number of targets or produce features with limited semantic information. To solve the aforementioned problems and allow the tracking of dozens of arbitrary objects in real-time, we propose SiamMOTION. SiamMOTION includes a novel proposal engine that produces quality features through an attention mechanism and a region-of-interest extractor fed by an inertia module and powered by a feature pyramid network. Finally, the extracted tensors enter a comparison head that efficiently matches pairs of exemplars and search areas, generating quality predictions via a pairwise depthwise region proposal network and a multi-object penalization module. SiamMOTION has been validated on five public benchmarks, achieving leading performance against current state-of-the-art trackers. Code available at: https://github.com/lorenzovaquero/SiamMOTION
