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Residual Transfer Learning for Multiple Object Tracking

机译:多对象跟踪的剩余转移学习

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摘要

To address the Multiple Object Tracking (MOT) challenge, we propose to enhance the tracklet appearance features, given by a Convolutional Neural Network (CNN), based on the Residual Transfer Learning (RTL) method. Considering that object classification and tracking are significantly different tasks at high level. And that traditional fine-tuning limits the possible variations in all the layers of the network since it changes the last convolutional layers. Beyond that, our proposed method provides more flexibility in terms of modelling the difference between these two tasks with a four-stage training. This transfer approach increases the feature performance compared to traditional CNN fine-tuning. Experiments on the MOT17 challenge show competitive results with the current state-of-the-art methods.
机译:为了解决多个对象跟踪(MOT)挑战,我们提出基于残余传输学习(RTL)方法来增强由卷积神经网络(CNN)给出的Tracklet外观特征。考虑到对象分类和跟踪在高水平下具有明显不同的任务。并且,传统的微调限制了网络的所有层中可能的变化,因为它改变了最后一个卷积层。除此之外,我们的建议方法在使用四阶段培训方面建模这些两项任务之间的差异提供了更大的灵活性。与传统的CNN微调相比,这种转移方法会增加特征性能。 MOT17挑战的实验表明,目前最先进的方法表现出具有竞争力的结果。

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