Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments

Rudenko, Andrey; Palmieri, Luigi; Doellinger, Johannes; Lilienthal, Achim J.; Arras, Kai O.

doi:10.1109/lra.2021.3062010

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Occupancy grid mapping is one of the most popular approaches for geographical mapping. Its usage is prominent in the domain of autonomous driving [14][15][16][17][18]. Mapping multiple sensors' information such as LiDAR, Radar and cameras to the surroundings of the vehicle in real time for the purpose of autonomous driving is a current topic.…”

Section: Probabilistic Occupancy Grid Mapping and Bayesian Fusionmentioning

confidence: 99%

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion

Hubner,

Wohlleben,

Litzenberger

et al. 2024

Sensors

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Effective security surveillance is crucial in the railway sector to prevent security incidents, including vandalism, trespassing, and sabotage. This paper discusses the challenges of maintaining seamless surveillance over extensive railway infrastructure, considering both technological advances and the growing risks posed by terrorist attacks. Based on previous research, this paper discusses the limitations of current surveillance methods, particularly in managing information overload and false alarms that result from integrating multiple sensor technologies. To address these issues, we propose a new fusion model that utilises Probabilistic Occupancy Maps (POMs) and Bayesian fusion techniques. The fusion model is evaluated on a comprehensive dataset comprising three use cases with a total of eight real life critical scenarios. We show that, with this model, the detection accuracy can be increased while simultaneously reducing the false alarms in railway security surveillance systems. This way, our approach aims to enhance situational awareness and reduce false alarms, thereby improving the effectiveness of railway security measures.

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Section: Probabilistic Occupancy Grid Mapping and Bayesian Fusionmentioning

confidence: 99%

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion

Hubner,

Wohlleben,

Litzenberger

et al. 2024

Sensors

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“…The recent work by Vemula et al [289] proposed an attention-based model that utilizes recurrent neural networks. For more in depth reading we recommend the survey by Rudenko et al [290], who have also extended the idea of human trajectory prediction to the problem of occupancy prediction [291]: instead of predicting individual trajectories, they infer pedestrian motion from semantic information about the environment.…”

Section: Trajectory Forecastingmentioning

confidence: 99%

A Survey on Human-aware Robot Navigation

Möller¹,

Furnari²,

Battiato³

et al. 2021

Preprint

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Intelligent systems are increasingly part of our everyday lives and have been integrated seamlessly to the point where it is difficult to imagine a world without them. Physical manifestations of those systems on the other hand, in the form of embodied agents or robots, have so far been used only for specific applications and are often limited to functional roles (e.g. in the industry, entertainment and military fields). Given the current growth and innovation in the research communities concerned with the topics of robot navigation, human-robot-interaction and human activity recognition, it seems like this might soon change. Robots are increasingly easy to obtain and use and the acceptance of them in general is growing. However, the design of a socially compliant robot that can function as a companion needs to take various areas of research into account. This paper is concerned with the navigation aspect of a socially-compliant robot and provides a survey of existing solutions for the relevant areas of research as well as an outlook on possible future directions.

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“…Visual object tracking is a fundamental research topic in computer vision and pattern recognition that has many applications, such as robot vision [1,2], video surveillance [3][4][5], medical-industrial integration [6][7][8], etc. The main task of visual tracking is to estimate the target's position and scale according to the given target specified by a bounding box in the first frame.…”

Section: Introductionmentioning

confidence: 99%

Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity

He,

Chen,

et al. 2023

Sensors

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Visual object tracking is a fundamental task in computer vision that requires estimating the position and scale of a target object in a video sequence. However, scale variation is a difficult challenge that affects the performance and robustness of many trackers, especially those based on the discriminative correlation filter (DCF). Existing scale estimation methods based on multi-scale features are computationally expensive and degrade the real-time performance of the DCF-based tracker, especially in scenarios with restricted computing power. In this paper, we propose a practical and efficient solution that can handle scale changes without using multi-scale features and can be combined with any DCF-based tracker as a plug-in module. We use color name (CN) features and a salient feature to reduce the target appearance model’s dimensionality. We then estimate the target scale based on a Gaussian distribution model and introduce global and local scale consistency assumptions to restore the target’s scale. We fuse the tracking results with the DCF-based tracker to obtain the new position and scale of the target. We evaluate our method on the benchmark dataset Temple Color 128 and compare it with some popular trackers. Our method achieves competitive accuracy and robustness while significantly reducing the computational cost.

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Learning Occupancy Priors of Human Motion From Semantic Maps of Urban Environments

Cited by 9 publications

References 35 publications

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion

Robust Detection of Critical Events in the Context of Railway Security Based on Multimodal Sensor Data Fusion

A Survey on Human-aware Robot Navigation

Scale-Aware Tracking Method with Appearance Feature Filtering and Inter-Frame Continuity

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