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首页> 外文会议>IEEE Conference on Computer Vision and Pattern Recognition Workshops >EgoTracker: Pedestrian Tracking with Re-identification in Egocentric Videos
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EgoTracker: Pedestrian Tracking with Re-identification in Egocentric Videos

机译：EgotRacker：在Egentric视频中重新识别的行人跟踪

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摘要

We propose and analyze a novel framework for tracking a pedestrian in egocentric videos, which is needed for analyzing social gatherings recorded with a wearable camera. The constant camera and pedestrian movement makes this a challenging problem. The main challenges are natural head movement of wearer and target loss and reappearance in a later frame, due to frequent changes in field of view. By using the optical flow information specific to egocentric videos and also by modifying the learning process and sampling region of trackers which tracks by learning an SVM online, we show that re-identification is possible. The specific trackers chosen are STRUCK and MEEM.
机译：我们提出并分析了一种用于跟踪自行电影的行人的新框架，这是分析用可穿戴相机记录的社交聚会所需的。恒定的相机和行人运动使这成为一个具有挑战性的问题。主要挑战是佩戴者的自然头部运动和在后面的框架中重新出现，由于视野的频繁变化。通过使用特定于EGENENTRIC视频的光学流量信息以及通过修改通过在线学习SVM的跟踪器的学习过程和采样区域，我们表明重新识别是可能的。选择的特定跟踪人员被击中和梅。

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