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A low resolution multi-camera system for person tracking

A low resolution multi-camera system for person tracking

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作者： Eldib, Mohamed Bo, Nyan Bo Deboeverie, Francis Nino, Jorge Guan, Junzhi Van De Velde, Samuel Steendam, Heidi Aghajan, Hamid Philips, Wilfried Department of Telecommunications and Information Processing Ghent University-iMinds Belgium Image Processing and Interpretation-Vision Systems United States Ambient Intelligence Research Lab Stanford University United States

ISBN: (纸本)9781479957514

The current multi-camera systems have not studied the problem of person tracking under low resolution constraints. In this paper, we propose a low resolution sensor network for person tracking. The network is composed of cameras with a resolution of 30×30 pixels. The multi-camera system is used to evaluate probability occupancy mapping and maximum likelihood trackers against ground truth collected by ultra-wideband (UWB) testbed. Performance evaluation is performed on two video sequences of 30 minutes. The experimental results show that maximum likelihood estimation based tracker outperforms the state-of-the-art on low resolution cameras. © 2014 IEEE.

关键词： Maximum likelihood estimation

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A low resolution multi-camera system for person tracking

A low resolution multi-camera system for person tracking

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IEEE International Conference on image processing

作者： Mohamed Eldib Nyan Bo Bo Francis Deboeverie Jorge Nino Junzhi Guan Samuel Van de Velde Heidi Steendam Hamid Aghajan Wilfried Philips Image Processing and Interpretation-Vision Systems Department of Telecommunications and Information Processing Ghent University-iMinds Belgium Ambient Intelligence Research Lab Stanford University USA

关键词： Cameras image resolution Correlation Maximum likelihood detection Maximum likelihood estimation Video sequences Tracking

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Robust multi-camera people tracking using maximum likelihood estimation

Robust multi-camera people tracking using maximum likelihood...

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15th International Conference on Advanced Concepts for Intelligent vision systems, ACIVS 2013

作者： Bo Bo, Nyan Van Hese, Peter Gruenwedel, Sebastian Guan, Junzhi Niño-Castañeda, Jorge Van Haerenborgh, Dirk Van Cauwelaert, Dimitri Veelaert, Peter Philips, Wilfried Image Processing and Interpretation/Vision Systems Ghent University/iMinds Ghent Belgium

ISBN: (纸本)9783319028941

This paper presents a new method to track multiple persons reliably using a network of smart cameras. The task of tracking multiple persons is very challenging due to targets' non-rigid nature, occlusions and environmental changes. Our proposed method estimates the positions of persons in each smart camera using a maximum likelihood estimation and all estimates are merged in a fusion center to generate the final estimates. The performance of our proposed method is evaluated on indoor video sequences in which persons are often occluded by other persons and/or furniture. The results show that our method performs well with the total average tracking error as low as 10.2 cm. We also compared performance of our system to a state-of-the-art tracking system and find that our method outperforms in terms of both total average tracking error and total number of object loss. © 2013 Springer-Verlag.

关键词： Maximum likelihood estimation

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PhD forum: Illumination-robust foreground detection for multi-camera occupancy mapping

PhD forum: Illumination-robust foreground detection for mult...

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2012 6th International Conference on Distributed Smart Cameras, ICDSC 2012

作者： Bo Bo, Nyan Gruenwedel, Sebastian Van Hese, Peter Niño-Castañeda, Jorge Oswaldo Van Haerenborgh, Dirk Van Cauwelaert, Dimitri Veelaert, Peter Philips, Wilfried Image Processing and Interpretation TELIN Ghent University/IBBT Belgium Vision Systems University College Ghent Belgium

ISBN: (纸本)9781450317726

Foreground detection is an essential preprocessing step for many image processing applications such as object tracking, human action recognition, pose estimation and occupancy mapping. Many existing techniques only perform well under steady illumination. Some approaches have been introduced to detect foreground under varying or sudden changes in illumination but the problem remains challenging. In this paper, we introduce a new texture-based foreground detection method which is robust to illumination change. Our method detects foreground by finding the correlation between the current frame and a background model. A region with low correlation is detected as foreground. We compare the performance of our proposed technique with other techniques from literature (edge-based, ViBe and Gaussian mixture model) as a preprocessing step of the multi-camera occupancy mapping system. The evaluation demonstrates that our technique outperforms the other methods in term of object loss. © 2012 ACM.

关键词： Mapping

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PhD forum: Correlation coefficient based template matching for indoor people tracking

PhD forum: Correlation coefficient based template matching f...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Junzhi Guan Peter Van Hese Jorge Oswaldo Niño-Castañeda Sebastian Gruenwedel Dirk Van Haerenborgh Dimitri Van Cauwelaert Peter Veelaert Wilfried Philips Image Processing and Interpretation Ghent University/IBBT Belgium Image Processing and Interpretation TELIN Ghent University/IBBT Belgium Vision Systems University College Ghent Belgium

One of the most popular methods to extract information from an image sequence is template matching. The principle of template matching is tracking a certain feature or target over time based on the comparison of the content of each frame with a simple template. In this article, we propose an correlation coefficient based template matching which is invariant to linear intensity distortions to do correction or verification of our existing indoor people tracking system [1].

关键词： Cameras Lighting Correlation Servers image processing Educational institutions Computer vision

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PhD forum: Illumination-robust foreground detection for multi-camera occupancy mapping

PhD forum: Illumination-robust foreground detection for mult...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Nyan Bo Bo Sebastian Gruenwedel Peter Van Hese Jorge Oswaldo Niño-Castañeda Dirk Van Haerenborgh Dimitri Van Cauwelaert Peter Veelaert Wilfried Philips Image Processing and Interpretation TELIN IBBT Ghent University Belgium Vision Systems University College Ghent Belgium

关键词： Lighting image edge detection Correlation Video recording Computational modeling Educational institutions

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Communicationless Navigation Through Robust Visual Odometry

Communicationless Navigation Through Robust Visual Odometry

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International IEEE Conference on Intelligent Transportation systems

作者： David Van Hamme Peter Veelaert Wilfried Philips Vision Systems research group University College Ghent Image Processing and Interpretation (IPI) research group department of Telecommunications and Information Processing (TELIN) Ghent University

ISBN: (纸本)9781467330640

GPS navigation is often found undependable in urban situations where tall structures occlude large parts of the sky. To keep accurate position in these situations, we need an alternative method. We propose a novel visual odometry method that is shown to provide reliable relative motion estimation in typical urban road driving using a single camera. While the short-term accuracy is good, relative motion estimation by itself is susceptible to drift and therefore insufficient to provide good long-term absolute position estimates. To overcome this drift, we propose to warp the visual odometry output to a stored map. This warping must be able to cope with temporary discrepancies between visual odometry and map data. The proposed mapping does not make a hard decision about road position, but instead entertains all plausible hypotheses about the followed trajectory and their associated warping costs. Evaluation on real test sequences proves that the method succesfully eliminates drift, and on average stays within 7 metres of simultaneously recorded GPS data. This proves that the combined visual odometry and mapping are sufficient to provide positioning with comparable accuracy to GPS in those situations when GPS is unavailable.

关键词： Cameras Visualization Vehicles Roads Trajectory Global Positioning System Feature extraction

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Robust monocular visual odometry by uncertainty voting

Robust monocular visual odometry by uncertainty voting

引用

IEEE Symposium on Intelligent Vehicle

作者： David Van Hamme Peter Veelaert Wilfried Philips Vision Systems research group University College Ghent Belgium Image Processing and Interpretation (IPI) research group Department of Telecommunications and Information Processing (TELIN) Ghent University Belgium

GPS by itself is not dependable in urban environments, due to signal reception issues such as multi-path effects or occlusion. Other sensor data is required to keep track of the vehicle in absence of a reliable GPS signal. We propose a new method to use a single on-board consumer-grade camera for vehicle motion estimation. The method is based on the tracking of ground plane features, taking into account the uncertainty on their backprojection as well as the uncertainty on the vehicle motion. A Hough-like parameter space vote is employed to extract motion parameters from the uncertainty models. The method is easy to calibrate and designed to be robust to outliers and bad feature quality. Experimental results show good accuracy and high reliability, with a positional estimate within 2 metres for a 400 metre elapsed distance.

关键词： Vehicles Cameras Visualization Uncertainty Feature extraction Trajectory Robustness

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Detection of a hand-raising gesture by locating the arm

Detection of a hand-raising gesture by locating the arm

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2011 IEEE International Conference on Robotics and Biomimetics, ROBIO 2011

作者： Bo, Nyan Bo Van Hese, Peter Van Cauwelaert, Dimitri Veelaert, Peter Philips, Wilfried Faculty of Technology and Environment Prince of Songkla University 80 Moo 1 Vichit-Songkram Road Kathu Phuket 83120 Thailand Image Processing and Interpretation Ghent University/IBBT St-Pietersnieuwstraat 41 B9000 Ghent Belgium Vision Systems University College Ghent Schoonmeersstraat 52 B9000 Ghent Belgium

ISBN: (纸本)9781457721373

This paper proposes a novel method for detecting hand-raising gestures in meeting room and classroom environments. The proposed method first detects faces in each frame of the video sequence in order to define the region of interest (ROI). Then the system locates arms in the region of interest by analyzing the geometric structure of edges on the arm instead of directly detecting the hand. The location and the orientation of a detected arm respect to the location of the face is used to make a decision on whether or not a person is raising hand. Finally, the frequency of a raised hand detected in previous frames is used to eliminate false positive detections and robustly detects persons who are raising a hand. Unlike major visual gesture recognition systems, our method does not rely on skin color or complex tracking algorithms, while achieving 92% sensitivity and 92% selectivity. © 2011 IEEE.

关键词： Location

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