中国科学院机构知识库网格系统: 检索

Towards Collaborative Robotics in Top View Surveillance: A Framework for Multiple Object Tracking by Detection Using Deep Learning 期刊论文 OAI收割
IEEE/CAA Journal of Automatica Sinica, 2021, 卷号: 8, 期号: 7, 页码: 1253-1270
作者: Imran Ahmed; Sadia Din; Gwanggil Jeon; Francesco Piccialli; Giancarlo Fortino

|

收藏 | 浏览/下载：29/0 | 提交时间：2021/06/11

Collaborative robotics deep learning object detection and tracking top view video surveillance

Design and development of autonomous robotic fish for object detection and tracking 期刊论文 OAI收割
International Journal of Advanced Robotic Systems, 2020, 卷号: 17, 期号: 3, 页码: 1-11
作者: Ji DX(冀大雄); Rehman, Faizan ur; Ajwad, Syed Ali; Shahani, K.; Sharma, Sanjay

|

收藏 | 浏览/下载：21/0 | 提交时间：2020/06/13

Robotic fish underwater vehicle object detection and tracking CFD analysis

Real-Time Lane-Vehicle Detection and Tracking System 会议论文 OAI收割
中国银川, 2016-5-28
作者: Huang G(黄冠); Wang Xingang; Wu Wenqi; Zhou Han; Wu Yuanyuan

|

收藏 | 浏览/下载：23/0 | 提交时间：2016/06/28

Lane Detection And Tracking Vehicle Detection Vehicle Tracking Convolutional Neural Networks

Online web video topic detection and tracking with semi-supervised learning 期刊论文 OAI收割
MULTIMEDIA SYSTEMS, 2016, 卷号: 22, 期号: 1, 页码: 115-125
作者: Li, Guorong; Jiang, Shuqiang; Zhang, Weigang; Pang, Junbiao; Huang, Qingming

|

收藏 | 浏览/下载：28/0 | 提交时间：2019/12/13

Topic detection and tracking Web video Multi-feature fusion Semi-supervised learning

Mining Semantic Context Information for Intelligent Video Surveillance of Traffic Scenes 期刊论文 OAI收割
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2013, 卷号: 9, 期号: 1, 页码: 149-160
作者: Zhang, Tianzhu; Liu, Si; Xu, Changsheng; Lu, Hanqing

收藏 | 浏览/下载：31/0 | 提交时间：2015/08/12

Event detection Gaussian mixture model (GMM) and graph cut object classification object detection object tracking video surveillance

社交网络中新闻挖掘的关键技术研究 学位论文 OAI收割
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2012
作者: 路荣

收藏 | 浏览/下载：55/0 | 提交时间：2015/09/02

社交网络检测与跟踪趋势预测能量函数趋势动量 SNS detection and tracking trend prediction energy function trend momentum

实时在线三维头部跟踪的研究及应用 学位论文 OAI收割
工学博士, 中国科学院自动化研究所: 中国科学院研究生院, 2011
作者: 王海波

收藏 | 浏览/下载：50/0 | 提交时间：2015/09/02

三维头部姿态跟踪基于梯度的跟踪基于检测的跟踪特征匹配与学习颜色先验运动颤抖消除眼神估计虚拟现实 3D head pose tracking gradient-based tracking tracking by detection feature matching and learning color prior motion jitter removing gaze estimation virtual reality

Evaluation of the operating range for ground-based infrared imaging tracking system (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Zhang Z.-D.

收藏 | 浏览/下载：48/0 | 提交时间：2013/03/25

Ground-based infrared imaging tracking system (GIITS) is of great importance for aerial target warning and guard. The operating range is one of the key performance specifications on the other which should be calculated calculate the radiation power received on the detector in order to analysis whether the output signal meets the detection requirements or not analyzed and studied during the whole GIITS design process. The operating range is mostly influenced by a few factors without considering the effect of the background radiation. By improving of the traditional method including atmospheric attenuation a new operating range calculation model of the GIITS was established based on two requirements. One is that the image size of observed target should meet the requirement of the processor signal extraction. The number of the pixel occupied by target image should be more than 9. The other is that the signal noise ratio (SNR) of the GIITS should not be less than 5 to meet the requirements of the target detection probability and spatial frequency. The SNR calculation equation in form of energy is deduced and the radiation characteristic of the observed target and background are analyzed. When evaluate the operating range of the GIITS using the new method the performance of GIITS and feature of target and background. This paper firstly makes analysis and summarization on the definite localizations of the traditional operating range equation of the GIITS. The localizations are mainly in two aspects. On one hand we should successively calculate two operating range values according to two requirements mentioned above and choose the minimum value as the analytic result. In the end the dispersion of the image and the effect of image dispersion are not considered in the traditional method an evaluation of operating range for fighter aircraft is accomplished as an example. The influence factors in every aspect on operating range were explored by the calculated result. The new operating range calculation model provides the theoretical basis for the design and applications as well as the comprehensive evaluation of a GIITS. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).

An automatic pedestrian detection and tracking method: Based on mach and particle filter (EI CONFERENCE) 会议论文 OAI收割
2011 International Conference on Network Computing and Information Security, NCIS 2011, May 14, 2011 - May 15, 2011, Guilin, Guangxi, China
Han Q.; Yao Z.

收藏 | 浏览/下载：16/0 | 提交时间：2013/03/25

This paper introduces a pedestrian detecting and tracking approach. Correlation filters present the composite properties which have been successively used in target detection. Particle filter are combined to locate the targets in real-time. Our contribution is proposing a general algorithm that is able to detect and track pedestrians in clutter environments. We also create a different view pedestrian dataset. Experiments show our algorithm is comparative when there is block and occlusion in tracking. 2011 IEEE.

Research on infrared dim-point target detection and tracking under sea-sky-line complex background (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Infrared Imaging and Applications, May 24, 2011 - May 24, 2011, Beijing, China
作者: Dong Y.-X.; Zhang H.-B.; Li Y.; Li Y.; Li Y.

收藏 | 浏览/下载：111/0 | 提交时间：2013/03/25

Target detection and tracking technology in infrared image is an important part of modern military defense system. Infrared dim-point targets detection and recognition under complex background is a difficulty and important strategic value and challenging research topic. The main objects that carrier-borne infrared vigilance system detected are sea-skimming aircrafts and missiles. Due to the characteristics of wide field of view of vigilance system the target is usually under the sea clutter. Detection and recognition of the target will be taken great difficulties.There are some traditional point target detection algorithms such as adaptive background prediction detecting method. When background has dispersion-decreasing structure the traditional target detection algorithms would be more useful. But when the background has large gray gradient such as sea-sky-line sea waves etc.The bigger false-alarm rate will be taken in these local area.It could not obtain satisfactory results. Because dim-point target itself does not have obvious geometry or texture feature in our opinion from the perspective of mathematics the detection of dim-point targets in image is about singular function analysis.And from the perspective image processing analysis the judgment of isolated singularity in the image is key problem. The foregoing points for dim-point targets detection its essence is a separation of target and background of different singularity characteristics.The image from infrared sensor usually accompanied by different kinds of noise. These external noises could be caused by the complicated background or from the sensor itself. The noise might affect target detection and tracking. Therefore the purpose of the image preprocessing is to reduce the effects from noise also to raise the SNR of image and to increase the contrast of target and background. According to the low sea-skimming infrared flying small target characteristics the median filter is used to eliminate noise improve signal-to-noise ratio then the multi-point multi-storey vertical Sobel algorithm will be used to detect the sea-sky-line so that we can segment sea and sky in the image. Finally using centroid tracking method to capture and trace target. This method has been successfully used to trace target under the sea-sky complex background. 2011 Copyright Society of Photo-Optical Instrumentation Engineers (SPIE).