摘要 针对当前视频目标跟踪算法跟踪多目标容易跟丢的问题,以视频中的装甲车、飞机为研究对象,研究一种改进跟踪学习检测(TLD)的视频多目标检测跟踪算法。对于跟丢的目标,利用Kalman滤波算法的预测功能跟踪视频中典型目标的轨迹,并采用Kalman滤波算法跟踪的轨迹来弥补TLD算法丢失的部分,从而获得视频中典型目标的完整轨迹,以提高视频多目标跟踪的准确率。由于现有轨迹预测算法存在准确性较差的局限性,提出一种基于社交长短时记忆(Social-LSTM)网络的视频典型目标轨迹预测算法,将上下文环境信息和多个目标轨迹之间的相互影响关系融入Social-LSTM网络,预测待检测典型目标的轨迹序列。仿真实验结果表明,所提轨迹预测算法优于传统的LSTM算法、隐马尔可夫模型算法以及混合高斯模型算法,有利于提高视频典型目标轨迹预测的准确率。 To solve the problem of easily losing the targets when tracking multiple targets by the current video target tracking algorithms,an improved tracking-learning-detection(TLD)algorithm is presented for multi-target detection and tracking by taking armored vehicles and aircrafts in videos as the research objects.For the lost targets,the trajectories of typical targets in video are tracked by using the prediction function of Kalman filtering algorithm,and the tracked trajectories are used to compensate for the lost parts of TLD algorithm so as to obtain the complete trajectories of typical targets in videos,which is beneficial to improve the accuracy of video multi-target tracking.For the poorer accuracy of the existing trajectory prediction methods,a video target trajectory prediction algorithm based on social long short term memory(Social-LSTM)network is proposed.The algorithm integrates the contextual environment information and the interaction relationship among multiple target trajectories into Social-LSTM network and predicts the trajectories of the typical targets to be detected.Simulation experimental results show the trajectory prediction algorithm is superior to the traditional LSTM algorithm,hidden Markov model(HMM)algorithm,and Gaussian mixture model(GMM)algorithm,which is helpful to improve the accuracy of trajectory prediction for typical video targets.
机构地区 北方工业大学信息学院 香港理工大学电子与信息工程系
出处 《兵工学报》 EI CAS CSCD 北大核心 2021年第3期545-554,共10页 Acta Armamentarii
基金 国家自然科学基金项目(61371143) 国家重点研发计划项目(2020YFC0811004) 北京市教育委员会基本科研业务费项目(110052971921/002) 教育部科技发展中心“天诚汇智”创新促教基金项目(2018A03029) 教育部高等教育司产学合作协同育人项目(201902083001) 北京市教育委员会科技项目(KM202110009002)。
关键词 装甲车 飞机 目标跟踪 多目标检测 社交长短时记忆网络 轨迹预测 armored vehicle aircraft object tracking multi-target detection social-long short term memory network trajectory prediction
