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In vivo observation of cells in the Arabidopsis thaliana root, by time-lapse confocal microscopy, is central to biology research. The research herein described is based on large amount of image data, which must be analyzed to determine the location and state of individual cells. Automating the process of cell tracking is an important step to create tools which will facilitate the analysis of cellspsila...
We propose an automatic method to geo-register maritime video feeds to satellite images. The method first detects horizon during the day time and apex during the night time for similarity rectification. It then finds water pixels on both satellite images and camera views. Finally, geo-registration is obtained by searching the best orientation where the water masks are matched. The current state-art-of-state...
In this paper, a novel multi-cue collaborative kernel tracking algorithm is proposed. A new constraint based on the property of cross ratio invariant enables tracking of objects insensitive to complex motions, including scale changes, rotation and especially views changes, without labeling and training. Meanwhile, invariant moments are introduced into the kernel based tracking method as the shape...
This work presents a novel technique for automatic detection and tracking of marine vehicles in video of open sea. The source of video is a video camera mounted on a buoy platform in open sea. Such system is intended to work autonomously, taking video of the surrounding ocean surface and analyzing them on presence of marine vehicles. The proposed technique is based on detection of marine vehicles...
This article presents a preceding car rear view tracking algorithm which utilizes a particle filter and belief function data fusion. Most of tracking applications resort to only one source of information, making the system dependent on the source reliability. To achieve more robust and longer tracking, multiple source data fusion is a solution. Belief functions are a powerful tool for data fusion...
This article introduces new similarity measures between trajectories, in order to detect uncommon behaviors. These measures are used to find the most common trajectories in a sequence, using an implicit aggregation method. They may be applied to trajectories of objects tracked in real time. Moreover, by combining one or more measures, it is possible to variate the impact of the temporal dimension...
Video-based target tracking is a challenging task, because there always appears to be complex occlusion among the varying number of objects. Also, in practice, it is very common that the objects in a scene move irregularly with abrupt turns, which results in an interesting heavy-tailed phenomenon. As simulation has to run exceptionally long enough to capture the effect of the distribution tail, it...
Wide area surveillance requires high-resolution images of the object of interest derived possibly from only low-resolution video of the whole scene. We propose a combined tracking and resolution enhancement approach that increases the resolution of the object of interest during tracking. The key idea is the use of an object-specific 3D mesh model with which we are able to track non-planar objects...
The same game is often played in real and virtual worlds. We integrate in-person and on-line playing of board games such as Go, bringing the real world into the virtual world. A player may record an in-person game by placing their camera on the table next to the game board, taking photos of the game. After automatically detecting the board and playing pieces, we perform inference on the time series...
Video surveillance systems produce huge amounts of data for storage and display. Long-term human monitoring of the acquired video is impractical and ineffective. Automatic abnormal motion detection system which can effectively attract operator attention and trigger recording is therefore the key to successful video surveillance in dynamic scenes, such as airport terminals. This paper presents a novel...
Video-surveillance systems are becoming more and more autonomous in the detection and the reporting of abnormal events. In this context, this paper presents an approach to detect abnormal situations in crowded scenes by analyzing the motion aspect instead of tracking subjects one by one. The proposed approach estimates sudden changes and abnormal motion variations of a set of points of interest (POI)...
In this paper an efficient method of small object localization is proposed that integrates detection and tracking. The system is initialized using a strong detector and then it locates the object over time using a weak detector and a temporal tracker. Both of strong and weak detectors are based on foreground-background segmentation. The strong detector is created from shape analysis of foreground...
The goal of this article is to present an effective and robust tracking algorithm for nonlinear feet motion by deploying particle filter integrated with Gaussian process latent variable model and embedded with Markov-switching approach. Training trajectory data is projected from the observation space to the latent space of lower dimensionality in a nonlinear probabilistic manner. In the latent space,...
We propose a new approach to estimate gait kinematics from image sequences taken by a monocular uncalibrated camera. This approach involves two generative models for gait representations in the kinematic and visual spaces, which induce two gait manifolds that characterize the gait variability in terms of the kinematics and visual appearance. A manifold topology enforcement scheme is introduced to...
The novelty of the approach presented in this paper is the unique object-based video coding framework for videos obtained from a static camera. As opposed to most existing methods, the proposed method does not require explicit 2D or 3D models of objects and hence is general enough to satisfy the need for varying types of objects in the scene. The proposed system detects and tracks an object in the...
Face detection and tracking, through image sequences, are primary steps in many applications such as video surveillance, human computer interface, and expression analysis. Many currently existing techniques donpsilat perform well due to pose variations, appearance changes, illumination changes, complex backgrounds, and inaccurate initialization. The last short coming, which is the difficulty to initialize...
We propose a Dynamic Bayesian Network (DBN) model for upper body tracking. We first construct a Bayesian Network (BN) to represent the human upper body structure and then incorporate into the BN various generic physical and anatomical constraints on the parts of the upper body. Unlike the existing upper body models, ours aims at handling physically feasible body motion rather than only some typical...
Traditional camera pedestals are manually operated. Our long term goal is to construct a fully autonomous pedestal system which can respond to changes in a scene and mimicking the human camera operator. In this paper, we discuss our experiments to control the vertical motion of a pedestal by leveling its position with a human head or a tracked hand-held object. We describe a set of computer vision...
This work presents a robust online learning and recognition system. The basic idea is to exploit information from tracking an object during the recognition and/or learning stage to obtain increased robustness and better recognition results. Object tracking by means of an extended MSER tracker is utilized to detect local features and construct their trajectories. Compact object representations are...
Ensembles of multiple (active) cameras yield an important ingredient in modern tracking and surveillance applications. They overcome the limited fields-of-view of single cameras, however, require robust procedures for handing over tracking tasks from one camera to another. In this paper a calibration-free procedure is proposed that allows for fast and reliable camera hand-over in Ambient Intelligence...
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