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Multi-pedestrian tracking based on feature learning method with lateral inhibition

机译:基于带侧向抑制特征学习方法的多行人跟踪

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摘要

As one of the hot issues in computer vision, multi-pedestrian tracking has received more and more attention recently. In this paper, under the tracking-by-detection framework, we propose a new feature learning method with lateral inhibition, combining with the traditional detection method, which is demonstrated to be effective. The tracking part utilizes a framework built upon particle filter, and the computation of the particle weight coordinately considers detector confidence, particle velocity and other factors. In addition, we carry out a procedure of particle variation before particle resampling to reduce the loss of particle diversity. As a bridge between the detector's output and the tracker's output, data association divides the original assignment into several independent branches for computation efficiency. Our algorithm has been shown to be feasible and effective after extensive experiments on some standard data sets.
机译:作为计算机视觉中的热点问题之一,多行人跟踪技术近来受到越来越多的关注。本文在基于检测的跟踪框架下,结合传统的检测方法,提出了一种新的具有侧向抑制的特征学习方法。跟踪部分利用建立在颗粒过滤器上的框架,颗粒重量的计算协调考虑了检测器的置信度,颗粒速度和其他因素。此外,我们在重新采样之前执行了粒子变化的过程,以减少粒子多样性的损失。作为检测器输出和跟踪器输出之间的桥梁,数据关联将原始分配分为几个独立的分支,以提高计算效率。在一些标准数据集上进行大量实验后,我们的算法被证明是可行和有效的。

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