Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

Stuede, Marvin; Schappler, Moritz

doi:10.48550/arxiv.2203.06911

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…We can find also unique measurements of a map quality like the Pearson correlation coefficient (Ak et al, 2018), Cramer-von-Mises criterion (Bennetts et al, 2019), Kullback-Leibler divergence (Rudenko et al, 2020), and k-NN Universal Divergence Estimator (Kucner et al, 2017). Rarely, we can find average probability density (Senanayake and Ramos, 2018;Senanayake et al, 2020) and chi-square distance (Vintr et al, 2019a;Molina et al, 2019;Stuede and Schappler, 2022).…”

Section: Benchmarking Spatio-temporal Mapsmentioning

confidence: 99%

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

et al. 2022

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Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.

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Section: Benchmarking Spatio-temporal Mapsmentioning

confidence: 99%

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

et al. 2022

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“…Contemporary approaches for activity mapping either employ highly complex computation models or artificial intelligence approaches for ROI detection. Marvin and Moritz [15] presented a non-parametric model for spatiotemporal activity based on Gaussian process regression (GPR). Sattar et al [16] proposed a convolution neural network (CNN)-based spatiotemporal activity mapping method for group activity detection.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches in [12][13][14] provide simple models for activity mapping and ROI detection; however, they lack the accuracy of ROI prediction. The methods proposed in [15][16][17][18][19] provide efficient spatiotemporal activity mapping for ROI detection, but at the cost of high computational complexity, and are thus not suitable for systems with low or limited resources. Artificial intelligence (AI)-based systems [16][17][18][19] require high computation and storage capabilities to train the model and further require iteratively changing the training model in unforeseen conditions, illumination changes, etc.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Activity Mapping for Enhanced Multi-Object Detection with Reduced Resource Utilization

Shashank

Sreedevi

2022

Electronics

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The accuracy of data captured by sensors highly impacts the performance of a computer vision system. To derive highly accurate data, the computer vision system must be capable of identifying critical objects and activities in the field of sensors and reconfiguring the configuration space of the sensors in real time. The majority of modern reconfiguration systems rely on complex computations and thus consume lots of resources. This may not be a problem for systems with a continuous power supply, but it can be a major set-back for computer vision systems employing sensors with limited resources. Further, to develop an appropriate understanding of the scene, the computer vision system must correlate past and present events of the scene captured in the sensor’s field of view (FOV). To address the abovementioned problems, this article provides a simple yet efficient framework for a sensor’s reconfiguration. The framework performs a spatiotemporal evaluation of the scene to generate adaptive activity maps, based on which the sensors are reconfigured. The activity maps contain normalized values assigned to each pixel in the sensor’s FOV, called normalized pixel sensitivity, which represents the impact of activities or events on each pixel in the sensor’s FOV. The temporal relationship between the past and present events is developed by utilizing standard half-width Gaussian distribution. The framework further proposes a federated optical-flow-based filter to determine critical activities in the FOV. Based on the activity maps, the sensors are re-configured to align the center of the sensors to the most sensitive area (i.e., region of importance) of the field. The proposed framework is tested on multiple surveillance and sports datasets and outperforms the contemporary reconfiguration systems in terms of multi-object tracking accuracy (MOTA).

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Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

Cited by 2 publications

References 16 publications

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation

Spatiotemporal Activity Mapping for Enhanced Multi-Object Detection with Reduced Resource Utilization

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