摘要
对于化工厂、电厂等重要场所,火灾、爆炸和有毒物质泄漏等安全生产举足轻重。因此对工业现场的监控至关重要。作为一种有效实时的视频目标跟踪算法,TLD算法(tracking-learning-detection)吸引了全世界的广泛关注。提出了一种PTLD的改进算法(prediction-tracking-learning-detection)。它是通过将卡尔曼预测器用于估计目标的位置以降低探测器的扫描区域,提高检测速度;增加基于目标运动方向的预测用于跟踪目标与背景相似的情况。通过增加位置和速度的预测并使用时空分析有效提高视频跟踪精度和速度。实验结果表明,PTLD算法为鲁棒实时的视频跟踪提供了一种方向。
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.
出处
《化工学报》
EI
CAS
CSCD
北大核心
2016年第3期967-973,共7页
CIESC Journal
基金
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)~~
关键词
预测
模型
时空分析
实时跟踪
prediction
model
algorithm
spatiotemporal analysis
real-time
