基于少量样本学习的多目标检测跟踪方法

Multi-Object Detection and Tracking Based on Few-Shot Learning

视频目标检测跟踪算法一直是计算机视觉领域的研究热点,目前大部分方法均需人工采集样本训练检测模型,搭建目标检测跟踪系统.当目标成像条件发生变化时,需重新采集样本,训练模型,调试整个检测跟踪系统,耗费大量人力、物力.本文提出一种基于少量样本学习的多目标检测跟踪算法,只需在监控视频第一帧指定待检测目标,即可自主生成混合分类模型,进行目标检测.采用在线渐进学习算法学习目标姿态变化,更新该模型.结合基于颜色的目标跟踪算法,自动构建高精度目标检测跟踪系统.整个过程无需手工采集、标注训练样本.因此,易于扩展到其它监控场景,通过自主学习形成该场景专用的检测跟踪系统,实现不同监控环境下,不同成像条件下都有较好的检测跟踪效果.实验表明,本方法能自主学习多种监控场景下的目标姿态,无需手工标注训练样本,在基于在线学习的算法中有最佳的检测精度,此外也取得了和离线目标检测跟踪系统相似的性能.

Video object detection and tracking algorithms have become the research focus in the field of computer vision.Traditional methods need to manually collect samples to train detection models,and build object detection and tracking systems.When the imaging conditions change,it is necessary to re-collect samples to train the detection model and re-adjust the entire detection and tracking system,which requires tedious human efforts.In this paper,a multi-object detection and tracking algorithm is proposed based on few-shot learning.With this approach,a hybrid classifier that models one object class can be generated by simply marking several bounding boxes around the object in the first video frame.An online gradual learning algorithm is proposed to learn the object pose changes and update the model.Combined with the color-based object tracking algorithm,our method automatically builds high-precision object detection and tracking systems without manual collection and labeling training samples.This approach can be conveniently replicated many times in different surveillance scenes and produce scene-specific detectors under various camera viewpoints.Experimental results on several video datasets show our approach achieves comparable performance to robust supervised methods,and outperforms the state-of-the-art online learning methods in varying imaging conditions.