Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Bellotto, Nicola; Hu, Huosheng

doi:10.1007/s10514-009-9167-2

Cited by 64 publications

(52 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…assuming that people keep moving with a constant speed. This is known to be a reasonable approximation for short-term behavior, used for collision avoidance [12,13] and tracking algorithms [14]. The method does not need any knowledge about the environment or the previous trajectory, but it is not reliable for long term prediction ignoring environmental constraints or influence .…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Ikeda

Chigodo

Rea

et al. 2012

Robotics: Science and Systems VIII

View full text Add to dashboard Cite

Abstract-This study addresses a method to predict pedestrians' long term behavior in order to enable a robot to provide them services. In order to do that we want to be able to predict their final goal and the trajectory they will follow to reach it. We attain this task borrowing from human science studies the concept of sub-goals, defined as points and landmarks of the environment towards which pedestrians walk or where they take directional choices before reaching the final destination. We retrieve the position of these sub-goals from the analysis of a large set of pedestrian trajectories in a shopping mall, and model their global behavior through transition probabilities between sub-goals. The method allows us to predict the future position of pedestrians on the basis of the observation of their trajectory up to the moment. 1

show abstract

Section: Related Workmentioning

confidence: 99%

“…Explicitly we assume where the probabilities in the summation term are obtained recursively from eq. (14). Finally, the probability to reach subgoal z given the observed sub-goal sequence and present preferred velocity, eq.…”

Section: ) Prediction Of Future Positionsmentioning

confidence: 99%

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Ikeda

Chigodo

Rea

et al. 2012

Robotics: Science and Systems VIII

View full text Add to dashboard Cite

show abstract

“…Acquired position data includes sensor noise stemming from the LRF accuracy, and the system noise stemming from the position acquisition process. So, we estimate the trajectory data by considering this noise using an EKF [29].…”

Section: Acquirement Of Pedestrian Trajectory Datamentioning

confidence: 99%

Model-based Pedestrian Trajectory Prediction using Environmental Sensor for Mobile Robots Navigation

Tonoki¹,

Yorozu²,

Takahashi³

2017

ijacsa

View full text Add to dashboard Cite

Abstract-Safety is the most important to the mobile robots that coexist with human. There are many studies that investigate obstacle detection and collision avoidance by predicting obstacles' trajectories several seconds into the future using mounted sensors such as cameras and laser range finder (LRF) for the safe behavior control of robots. In environments such as crossing roads where blind areas occur because of visual barriers like walls, obstacle detection might be delayed and collisions might be difficult to avoid. Using environmental sensors to detect obstacles is effective in such environments. When crossing roads, there are several passages pedestrian might move and it is difficult to depict going each passage in the same movement model. Therefore, we hypothesize that a more effective way to predict pedestrian movement is by predicting passages pedestrian might move and estimating the trajectories to the passages. We acquire pedestrian trajectory data using an environmental LRF with an extended Kalman filter (EKF) and construct pedestrian movement models using vector auto regressive (VAR) models, which pedestrian state is consisting of the position, speed and direction. Then, we test the validity of the constructed pedestrian movement models using experimental data. We narrow down the selection of a pedestrian movement model by comparing the prediction error for each path between the estimated pedestrian state using an EKF, and the predicted state using each movement model. We predict the trajectory using the selected movement model. Finally, we confirm that an appropriate path model that a pedestrian can actually move through is selected before the crossing area and that only the appropriate model is selected near the crossing area.

show abstract

“…To perform data association from motion and detection, the Global Nearest Neighbor approach 28 (solved with the Munkres algorithm) is adopted. The cost matrix derives from the evaluation where it is used as a two-term joint likelihood for every target-detection couple.…”

Section: Data Associationmentioning

confidence: 99%

People detection and tracking using RGB-D cameras for mobile robots

Liu

Luo

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

People detection and tracking is an essential capability for mobile robots in order to achieve natural human-robot interaction. In this article, a human detection and tracking system is designed and validated for mobile robots using color data with depth information RGB-depth (RGB-D) cameras. The whole framework is composed of human detection, tracking and re-identification. Firstly, ground points and ceiling planes are removed to reduce computation effort. A prior-knowledge guided random sample consensus fitting algorithm is used to detect the ground plane and ceiling points. All left points are projected onto the ground plane and subclusters are segmented for candidate detection. Meanshift clustering with an Epanechnikov kernel is conducted to partition different points into subclusters. We propose the new idea of spatial region of interest plan view maps which are employed to identify human candidates from point cloud subclusters. Here, a depthweighted histogram is extracted online to feature a human candidate. Then, a particle filter algorithm is adopted to track the human's motion. The integration of the depth-weighted histogram and particle filter provides a precise tool to track the motion of human objects. Finally, data association is set up to re-identify humans who are tracked. Extensive experiments are conducted to demonstrate the effectiveness and robustness of our human detection and tracking system.

show abstract

Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters

Cited by 64 publications

References 21 publications

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Modeling and Prediction of Pedestrian Behavior based on the Sub-goal Concept

Model-based Pedestrian Trajectory Prediction using Environmental Sensor for Mobile Robots Navigation

People detection and tracking using RGB-D cameras for mobile robots

Contact Info

Product

Resources

About