Fast visual people tracking using a feature-based people detector

Königs, Achim; Schulz, Dirk

doi:10.1109/iros.2011.6094902

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…In [18], they presented a tracking algorithm that combines the mean‐shift and particle‐Kalman filter. In [38], they introduce an approach that makes human detection by the fast version of an implicit shape model detector, which is trained on people. In [39], they proposed the tracker based on the point matching method; the point‐based features will be extracted first and be corrected by the DBScan algorithm, and then construct an object model as the tracker.…”

Section: Resultsmentioning

confidence: 99%

A robust tracking algorithm for a human‐following mobile robot

Tsai

Yao

2021

IET Image Processing

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The capability of recognizing and tracking a specific human is considered a key technique for serving mobile robots. This paper proposes a real‐time tracker that uses a stereo camera to track specific people in different environments. A tracker has been designed to detect the selected target from multiple people by use of block matching algorithm, human detection, and colour histogram comparison. Considering the similar colour of the objects, the predictor of the Kalman filter determines the position of the object. Finally, the mobile robot is moved according to the tracking results with a relative distance between the robot and the target. The effectiveness of the proposed method is demonstrated by experiments on many video sequences and real environments.

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Section: Resultsmentioning

confidence: 99%

A robust tracking algorithm for a human‐following mobile robot

Tsai

Yao

2021

IET Image Processing

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“…The fifth assumption has been observed in social studies carried out on human motion behavior (Nourbakhsh et al, 2003). The sixth assumption is difficult to verify at the present time because, although there exist several algorithms which can detect people's position, the methods are not yet precise and robust enough for situations in which people are partially or completely out of the robots's field of vision, for example (Konigs and Schulz, 2011). The next three assumptions are valid and have been tested in outdoor real-life scenarios (Murtra et al, 2008).…”

Section: Problem Constraints and Model Assumptionsmentioning

confidence: 90%

Cooperative social robots to accompany groups of people

Garrell¹,

Sanfeliu²

2012

The International Journal of Robotics Research

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This study proposes a new model for guiding people in urban settings using multiple robots that work cooperatively. More specifically, this investigation describes the circumstances in which people might stray from the formation when following different robots' instructions. To this end, we introduce a "Prediction and Anticipation Model" that predicts the position of the group using a Particle Filter, while determining the optimal robot behavior to help people stay in the group in areas where they may become distracted. As a result, this article presents a novel approach to locally optimizing the work performed by robots and people using the minimum robots' work criterion and determining human-friendly types of movements. The guidance missions were carried out in urban areas that included multiple conflict areas and obstacles. This study also provides an analysis of robots' behavioral reactions to people by simulating different situations in the locations that were used for the investigation. The method was tested through simulations that took into account the difficulties and technological constraints derived from real-life situations. Despites these problematic issues, we were able to demonstrate the robots' effect on people in real-life situations in terms of pushing and dragging forces.

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