971 resultados para Video tracking
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In this paper, we describe a video tracking application using the dual-tree polar matching algorithm. The models are specified in a probabilistic setting, and a particle ilter is used to perform the sequential inference. Computer simulations demonstrate the ability of the algorithm to track a simulated video moving target in an urban environment with complete and partial occlusions. © The Institution of Engineering and Technology.
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We present a user supported tracking framework that combines automatic tracking with extended user input to create error free tracking results that are suitable for interactive video production. The goal of our approach is to keep the necessary user input as small as possible. In our framework, the user can select between different tracking algorithms - existing ones and new ones that are described in this paper. Furthermore, the user can automatically fuse the results of different tracking algorithms with our robust fusion approach. The tracked object can be marked in more than one frame, which can significantly improve the tracking result. After tracking, the user can validate the results in an easy way, thanks to the support of a powerful interpolation technique. The tracking results are iteratively improved until the complete track has been found. After the iterative editing process the tracking result of each object is stored in an interactive video file that can be loaded by our player for interactive videos.
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INTRODUCTION: The EVA (Endoscopic Video Analysis) tracking system a new tracking system for extracting motions of laparoscopic instruments based on non-obtrusive video tracking was developed. The feasibility of using EVA in laparoscopic settings has been tested in a box trainer setup. METHODS: EVA makes use of an algorithm that employs information of the laparoscopic instrument's shaft edges in the image, the instrument's insertion point, and the camera's optical centre to track the 3D position of the instrument tip. A validation study of EVA comprised a comparison of the measurements achieved with EVA and the TrEndo tracking system. To this end, 42 participants (16 novices, 22 residents, and 4 experts) were asked to perform a peg transfer task in a box trainer. Ten motion-based metrics were used to assess their performance. RESULTS: Construct validation of the EVA has been obtained for seven motion-based metrics. Concurrent validation revealed that there is a strong correlation between the results obtained by EVA and the TrEndo for metrics such as path length (p=0,97), average speed (p=0,94) or economy of volume (p=0,85), proving the viability of EVA. CONCLUSIONS: EVA has been successfully used in the training setup showing potential of endoscopic video analysis to assess laparoscopic psychomotor skills. The results encourage further implementation of video tracking in training setups and in image guided surgery.
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We present a novel approach for multi-object detection in aerial videos based on tracking. The proposed method mainly involves three steps. Firstly, the spatial-temporal saliency is employed to detect moving objects. Secondly, the detected objects are tracked by mean shift in the subsequent frames. Finally, the saliency results are fused with the weight map generated by tracking to get refined detection results, and in turn the modified detection results are used to update the tracking models. The proposed algorithm is evaluated on VIVID aerial videos, and the results show that our approach can reliably detect moving objects even in challenging situations. Meanwhile, the proposed method can process videos in real time, without the effect of time delay.
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Campylobacter jejuni is a prevalent cause of food-borne diarrhoeal illness in humans. Understanding of the physiological and metabolic capabilities of the organism is limited. We report a detailed analysis of the C. jejuni growth cycle in batch culture. Combined transcriptomic, phenotypic and metabolic analysis demonstrates a highly dynamic 'stationary phase', characterized by a peak in motility, numerous gene expression changes and substrate switching, despite transcript changes that indicate a metabolic downshift upon the onset of stationary phase. Video tracking of bacterial motility identifies peak activity during stationary phase. Amino acid analysis of culture supernatants shows a preferential order of amino acid utilization. Proton NMR (1H-NMR) highlights an acetate switch mechanism whereby bacteria change from acetate excretion to acetate uptake, most probably in response to depletion of other substrates. Acetate production requires pta (Cj0688) and ackA (Cj0689), although the acs homologue (Cj1537c) is not required. Insertion mutants in Cj0688 and Cj0689 maintain viability less well during the stationary and decline phases of the growth cycle than wild-type C. jejuni, suggesting that these genes, and the acetate pathway, are important for survival.
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本文以跟踪电视系统中自适应量化器为设计背景,提出了一种新的、实时自适应的快速图象量化方法——逐极均值法,文中首先用Lloyd-Max最佳量化理论分析了这种量化方法的均方误差失真,讨沦了图象中存在孤立亮点时的处理方法。然后论述了这种量化方法应用于跟踪电视系统中的性能,即实现的简单、快速性;对照度变化的自适应性;及图象对比度增强效果。文中通过图象处理实验结果验证了这种量化方法的性能和理论分析的正确性。最后得出结论:逐极均值法量化器是一种能够代替LlodyMax最佳量化器的次佳量化器,这种量化器可以很好地满足跟踪电视系统中对自适应量化器的设计所提出的各方面性能要求;它对那些要求实现简单、实时自适应的量化器应用领域也将具有一定意义。
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Cette thése a été réalisée dans le cadre d'une cotutelle avec l'Institut National Polytechnique de Grenoble (France). La recherche a été effectuée au sein des laboratoires de vision 3D (DIRO, UdM) et PERCEPTION-INRIA (Grenoble).
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La vidéosurveillance a pour objectif principal de protéger les personnes et les biens en détectant tout comportement anormal. Ceci ne serait possible sans la détection de mouvement dans l’image. Ce processus complexe se base le plus souvent sur une opération de soustraction de l’arrière-plan statique d’une scène sur l’image. Mais il se trouve qu’en vidéosurveillance, des caméras sont souvent en mouvement, engendrant ainsi, un changement significatif de l’arrière-plan; la soustraction de l’arrière-plan devient alors problématique. Nous proposons dans ce travail, une méthode de détection de mouvement et particulièrement de chutes qui s’affranchit de la soustraction de l’arrière-plan et exploite la rotation de la caméra dans la détection du mouvement en utilisant le calcul homographique. Nos résultats sur des données synthétiques et réelles démontrent la faisabilité de cette approche.
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Senior thesis written for Oceanography 445
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Transient prenatal vitamin D deficiency produces hyperlocomotion in the adult rat. The aim of this study was to examine the effects of acute restraint on the behaviour of DVD and control rats in the open field. Rats were conceived and born to developmentally vitamin D (DVD) deficient or replete (control) dams and, at 8 weeks of age, were monitored for 30 min in an open field using automated video tracking software. Half of the rats were restrained within a towel for 5 min immediately before the open field test. The remainder received minimal handling prior to the open field test. Repeating previous findings, DVD deficient animals had enhanced locomotion during the first 10 min of the open field test compared to control rats. By contrast, there were no differences in locomotor activity after acute restraint stress. The time rats spent in the corners and side of the open field was affected by prenatal diet. DVD rats spent less time in the corners and more time in the side than control rats across the whole 30 min test. This difference was not seen in rats with acute restraint stress. The time spent in the centre was not altered by prenatal diet or acute restraint. Thus, transient prenatal vitamin D deficiency induces a transient spontaneous hyperlocomotion in adulthood that is modulated by acute restraint stress. (c) 2006 Elsevier B.V. All rights reserved.
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This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.