Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation
Multiple Object Tracking with Mixture Density Networks for Trajectory Estimation
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发表时间：
2021-06
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通讯作者：
Andreu Girbau;Xavier Gir'o-i-Nieto;Ignasi Rius;F. Marqu'es
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作者：
Andreu Girbau;Xavier Gir'o-i-Nieto;Ignasi Rius;F. Marqu'es
多对象跟踪面临着几个挑战，可以用轨迹信息来缓解。知道物体的后位置有助于消除歧义和解决诸如遮挡、重新识别和身份切换等情况。在这项工作中，我们表明，轨迹估计可以成为跟踪的一个关键因素，并提出TrajE，一个基于递归混合密度网络的轨迹估计器，作为一个通用模块，可以添加到现有的对象跟踪器。为了提供几个轨迹假设，我们的方法使用波束搜索。此外，依赖于相同的估计轨迹，我们建议在发生遮挡后重建轨迹。我们将TrajE集成到两种最先进的跟踪算法中，CenterTrack [63]和Tracktor [3]。他们在MOTChallenge 2017测试集中的表现分别在MOTA得分中提升了6.3和0.3分，在IDF1中提升了1.8和3.1分，为CenterTrack+TrajE配置创造了新的艺术水平
Multiple object tracking faces several challenges that may be alleviated with trajectory information. Knowing the posterior locations of an object helps disambiguating and solving situations such as occlusions, re-identification, and identity switching. In this work, we show that trajectory estimation can become a key factor for tracking, and present TrajE, a trajectory estimator based on recurrent mixture density networks, as a generic module that can be added to existing object trackers. To provide several trajectory hypotheses, our method uses beam search. Also, relying on the same estimated trajectory, we propose to reconstruct a track after an occlusion occurs. We integrate TrajE into two state of the art tracking algorithms, CenterTrack [63] and Tracktor [3]. Their respective performances in the MOTChallenge 2017 test set are boosted 6.3 and 0.3 points in MOTA score, and 1.8 and 3.1 in IDF1, setting a new state of the art for the CenterTrack+TrajE configuration