基于PTLD的长时间视频跟踪算法
Long-term visual tracking using PTLD algorithm
摘要
Abstract
Along with such dangerous sources as big fire, explosion and toxic matter leak in the chemical plants, the visual tracking technology is a simple yet effective solution. As an effective real-time visual target tracking algorithm, the tracking-learning-detection (TLD) has drawn wide attention around the world. In this paper, we propose a prediction-tracking-learning-detection (PTLD) based visual target tracking algorithm, which is obtained by making several improvements based on the original TLD algorithm. The improvements include employing Kalman filter in the detector of TLD for estimating the location of the target to reduce the scanning region of the detector and improve the speed of the detector; adding Markov model based target moving direction predictor in the detector of TLD to increase the discretion for target with similar appearance. In addition to ascending in the tracking speed by increasing the position and speed prediction, we use the spatiotemporal analysis that also greatly improves the tracking precision. Experimental results show that the proposed PTLD algorithm provides a means for robust real-time visual tracking.关键词
预测/模型/时空分析/实时跟踪Key words
prediction/model/algorithm/spatiotemporal analysis/real-time分类
数理科学引用本文复制引用
刘建,郝矿荣,丁永生,杨诗宇..基于PTLD的长时间视频跟踪算法[J].化工学报,2016,67(3):967-973,7.基金项目
国家自然科学基金重点项目（61134009）；国家自然科学基金项目（61473077，61473078，61503075）；国家自然科学基金海外及港澳学者合作研究基金项目（61428302）；教育部长江学者奖励计划项目；上海领军人才专项资金；上海市科学技术委员会重点基础研究项目（13JC1407500）；上海市教育委员会科研创新项目（14ZZ067）；上海市浦江人才计划项目（15PJ1400100）；中央高校基本科研业务费专项资金（15D110423，2232015D3-32）。@@@@supported by the Key Project of the National Natural Science Foundation of China (61134009), the National Natural Science Foundation of China (61473077,61473078,61503075), the Cooperative Research Funds of the National Natural Science Funds Overseas and Hong Kong and Macao Scholars (61428302), the Program for Changjiang Scholars from the Ministry of Education, the Specialized Research Fund for Shanghai Leading Talents, the Project of the Shanghai Committee of Science and Technology (13JC1407500), the Innovation Program of Shanghai Municipal Education Commission (14ZZ067), Shanghai Pujiang Program (15PJ1400100) and the Fundamental Research Funds for the Central Universities (15D110423,2232015D3-32) （61134009）
