摘要
近年来,深度学习在计算机视觉领域的应用取得了突破性进展,但基于深度学习的视频多目标跟踪(Multiple object tracking, MOT)研究却相对甚少,而鲁棒的关联模型设计是基于检测的多目标跟踪方法的核心.本文提出一种基于深度神经网络和度量学习的关联模型:采用行人再识别(Person re-identification, Re-ID)领域中广泛使用的度量学习技术和卷积神经网络(Convolutional neural networks, CNNs)设计目标外观模型,即利用三元组损失函数设计一个三通道卷积神经网络,提取更具判别性的外观特征构建目标外观相似度;再结合运动模型计算轨迹片间的关联概率.在关联策略上,采用匈牙利算法,首先以逐帧关联方式得到短小可靠的轨迹片集合,再通过自适应时间滑动窗机制多级关联,输出各目标最终轨迹.在2DMOT2015、MOT16公开数据集上的实验结果证明了所提方法的有效性,与当前一些主流算法相比较,本文方法取得了相当或者领先的跟踪效果.
While deep learning has made a breakthrough in many sub-fields of computer vision recently, there are only a few deep learning approaches to multiple object tracking(MOT). Since the key component in detection based multiple object tracking is to design a robust affinity model, this paper proposes a novel affinity model based on deep neural network and metric learning, that is, metric learning, a widely used technique in the task of person re-identification(ReID), is exploited with convolutional neural networks(CNNs) to design the object s appearance model. Specifically, we adopt a three-channel CNNs that is learned by triplet loss function, to extract the discriminative appearance features and compute appearance similarity between objects. The appearance affinity is then combined with motion model to estimate associating probability among trajectories. A hierarchical association strategy is employed by the Hungarian algorithm.At the low level, a set of short but reliable tracklets are generated in a frame by frame fashion. These tracklets are then further associated to form longer tracklets at the higher levels via an adaptive sliding-window mechanism. Experiment results in the challenging MOT benchmark demonstrate the validity of the proposed method. Compared with several state-of-the-art approaches, our method has achieved competitive or superior performance.
作者
侯建华
张国帅
项俊
HOU Jian-Hua;ZHANG Guo-Shuai;XIANG Jun(College of Electronic Information Engineering,South-Central University for Nationalities,Wuhan 430074)
出处
《自动化学报》
EI
CSCD
北大核心
2020年第12期2690-2700,共11页
Acta Automatica Sinica
基金
国家自然科学基金(61671484,61701548)
湖北省自然科学基金(2018CFB503)
中南民族大学中央高校基本科研业务费专项资金项目(CZ Q17001,CZZ18001,CZY18046)资助。
关键词
多目标跟踪
深度学习
度量学习
关联模型
多级关联
Multiple object tracking(MOT)
deep learning
metric learning
affinity model
multi-level association
