4 research outputs found

    Research on The Behavior Identification of Pig Delivery And Piglet Tracking in Video Surveillance

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    利用智能视频监控手段替代人工观察,准确实时地检测母猪分娩,同时对出生仔猪的运动状态进行判别是很有价值的研究课题,此课题正是相关企业新产品委托研发中的主要关键技术之一。本文给出了实际视频监控场景下的猪只分娩行为识别方法,并对出生的仔猪进行了跟踪。本文主要的研究工作有: 1.前景检测。首先研究对比了计算机视觉中常见的三种前景检测算法:GMM、Vibe和帧间差分法。在将它们分别应用到实际的猪只行为视频上后,经分析和对比实验结果,对前景检测方法做出改进。在改进后的基础上,运用图像处理中的形态学操作,获得更好的前景检测结果。 2.猪只分娩行为识别。本文给出了一种基于图像分割和霍夫变换的母猪位置获取和...Detecting real time sow delivery and judging the movement state of birth piglets accurately by using intelligent monitoring instead of human observation are valuable and significant task in this field. And this topic is one of the main key technologies in a new product research and development of enterprise cooperation project. In this thesis, we propose the methods for recognizing the behaviors o...学位:工程硕士院系专业:信息科学与技术学院_工程硕士(计算机技术)学号:2302014115317

    Target Tracking System Constructed by ELM-AE and Transfer Representation Learning

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    In the target tracking algorithm, the feature model’s ability to quickly learn image features and the ability to adapt to changes in target features during tracking has always been one of the main research directions of target tracking algorithms. Especially for discriminative target trackers based on image block learning, these two points have become decisive factors affecting the efficiency and robustness of the tracker. However, the performance of most existing similar algorithms on these two abilities cannot achieve satisfactory results. To solve this problem, an efficient and robust feature model is proposed. The feature model first uses extreme learning machine autoencoder (ELM-AE) to quickly perform random feature mapping on complex image features of the target and background image blocks, and then uses the transfer learning ability of transfer representation learning (TRL) to improve the adaptability of random feature space. The feature model is named transfer representation learning with ELM-AE (TRL-ELM-AE). Compared with original complex image features, this model can provide the classifier with more compact and expressive shared features, so that the classifier can learn and classify more quickly and efficiently. In addition, in the target tracking process, the target and background usually change continuously over time. Although the feature migration capability of TRL can already adapt to this, in order to further improve the robustness of the tracker, a strategy of dynamically updating training samples is adopted. Through a large number of experimental and analysis results on the 11 target tracking challenge scenarios proposed by OTB, it is proven that the proposed target tracker has significant advantages over the existing target tracker

    Architecture and key technologies of coalmine underground vision computing

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    It has always been a common demand to stay away from the harsh environment with narrow space, numerous devices, complex operation process, and hidden hazards, and realize intelligent unmanned mining in the coal industry. To achieve this goal, it is very necessary for us to develop an effective theory of vision computing for underground coalmine applications. Its main task is to build effective models or frameworks for perceiving, describing, recognizing and understanding the environment of underground coalmine, and let intelligent equipment get 3D environment information in coalmine from images or videos. To effectively develop this theory and make it better for intelligent development of coalmine, this paper first analyzed the similarities and differences about computer vision and visual computing in coalmine, and proposed its composition architecture. And then, this paper introduced in detail the key technologies involved in visual computing in coalmine including visual perception and light field computing, feature extraction and feature description, semantic learning and vision understanding, 3D vision reconstruction, and sense computing integration and edge intelligence, which is followed by typical application cases of visual computing in coalmines. Finally, the development trend and prospect of underground visual computing in coalmine was given. In this section, this paper focused on concluding the key challenges and introducing two valuable applications including coalmine Augmented Reality/Mixed Reality and parallel intelligent mining. With the breakthrough of underground vision computing, it will play a more and more important role in the intelligent development of coal mines
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