Comparison of Laser-Based Person Tracking at Feet and Upper-Body Height

Schenk, Konrad; Eisenbach, Markus; Kolarow, Alexander; Groß, Horst–Michael

doi:10.1007/978-3-642-24455-1_27

Cited by 13 publications

(5 citation statements)

References 12 publications

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“…Additionally, we showed that this approach is robust enough to realize visual person following (visual servoing) on a mobile robot. In our future work, we will implement the presented method in combination with laser range finders [24], [25] and sensor fusion algorithms into a general tracking and evaluation framework for robotic applications.…”

Section: Discussionmentioning

confidence: 99%

Vision-based hyper-real-time object tracker for robotic applications

Kolarow

Brauckmann²,

Eisenbach

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Fast vision-based object and person tracking is important for various applications in mobile robotics and HumanRobot Interaction. While current state-of-the-art methods use descriptive features for visual tracking, we propose a novel approach using a sparse template based feature set, which is drawn from homogeneous regions on the object to be tracked.Using only a small number of simple features, without complex descriptors in combination with logarithmic-search, the tracker performs at hyper-real-time on HD-images without the use of parallelized hardware. Detailed benchmark experiments show that it outperforms most other state-of-the-art approaches for real-time object and person tracking in quality and runtime. In the experiments we also show the robustness of the tracker and evaluate the effects of different initialization methods, feature sets, and parameters on the tracker. Although we focus on the scenario of person and object tracking in robot applications, the proposed tracker can be used for a variety of other tracking tasks.

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Section: Discussionmentioning

confidence: 99%

Vision-based hyper-real-time object tracker for robotic applications

Kolarow

Brauckmann²,

Eisenbach

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…It is generated in an offline phase by clustering trajectories, obtained by camera [11] and laser-range-finder based people tracking [14,15]. The mean transition time and variance between neighboring nodes is stored in their connecting edge.…”

Section: Predictionmentioning

confidence: 99%

APFel: The intelligent video analysis and surveillance system for assisting human operators

Kolarow

Schenk

Eisenbach

et al. 2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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The rising need for security in the last years has led to an increased use of surveillance cameras in both public and private areas. The increasing amount of footage makes it necessary to assist human operators with automated systems to monitor and analyze the video data in reasonable time. In this paper we summarize our work of the past three years in the field of intelligent and automated surveillance. Our proposed system extends the common active monitoring of camera footage into an intelligent automated investigative person-search and walk path reconstruction of a selected person within hours of image data. Our system is evaluated and tested under life-like conditions in real-world surveillance scenarios. Our experiments show that with our system an operator can reconstruct a case in a fraction of time, compared to manually searching the recorded data.

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“…6). An external multi-laser tracking system [24] was used to track the persons' 2D ground truth positions. Each person's height was measured manually once.…”

Section: B Upper-body Pose and Person Re-identificationmentioning

confidence: 99%

Appearance-Based 3D Upper-Body Pose Estimation and Person Re-identification on Mobile Robots

Weinrich

Volkhardt

Groß

2013

2013 IEEE International Conference on Systems, Man, and Cybernetics

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In the field of human-robot interaction (HRI), detection, tracking and re-identification of humans in a robot's surroundings are crucial tasks, e. g. for socially compliant robot navigation. Besides the 3D position detection, the estimation of a person's upper-body orientation based on monocular camera images is a challenging problem on a mobile platform. To obtain real-time position tracking as well as upper-body orientation estimations, the proposed system comprises discriminative detectors whose hypotheses are tracked by a Kalman filter-based multihypotheses tracker. For appearance-based person recognition, a generative approach, based on a 3D shape model, is used to refine these tracked hypotheses. This model evaluates edges and color-based discrimination from the background. Furthermore, for each person the texture of his or her upper-body is learned and used for person re-identification. When computational resources are limited, the update rate of the model-based optimization reduces itself automatically. Thereby the estimation accuracy decreases, but the system keeps tracking the persons around the robot in real-time. The person's 3D pose is tracked up to a distance of 5.0 meters with an average Euclidean error of 18 cm. The achieved motion independent average upper-body orientation error is 22 • . Furthermore, the upper-body texture is learned on-line which allowed a stable person re-identification in our experiments.

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Comparison of Laser-Based Person Tracking at Feet and Upper-Body Height

Cited by 13 publications

References 12 publications

Vision-based hyper-real-time object tracker for robotic applications

Vision-based hyper-real-time object tracker for robotic applications

APFel: The intelligent video analysis and surveillance system for assisting human operators

Appearance-Based 3D Upper-Body Pose Estimation and Person Re-identification on Mobile Robots

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