Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

Saleem, Noor Haitham; Chien, Hsiang-Jen; Rezaei, Mahdi; Klette, Reinhard

doi:10.1109/tits.2018.2879429

Cited by 10 publications

(5 citation statements)

References 51 publications

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“…Stereo-vision is a popular technique for distance estimation such as in 75 ; however, this is not a feasible approach in our research when we aim at integration of an efficient solution, applicable in all public places using only a basic CCTV camera. Therefore we adhere to a monocular solution.…”

Section: Methodsmentioning

confidence: 99%

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Rezaei

Azarmi

2020

Preprint

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Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras. The proposed model includes a YOLOv4-based framework and inverse perspective mapping for accurate people detection and social distancing monitoring in challenging conditions, including people occlusion, partial visibility, and lighting variations. We also provide an online risk assessment scheme by statistical analysis of the Spatio-temporal data from the moving trajectories and the rate of social distancing violations. We identify high-risk zones with the highest possibility of virus spread and infections. This may help authorities to redesign the layout of a public place or to take precaution actions to mitigate high-risk zones. The efficiency of the proposed methodology is evaluated on the Oxford Town Centre dataset, with superior performance in terms of accuracy and speed compared to three state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

Rezaei

Azarmi

2020

Preprint

Self Cite

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“…Stereoscopic vision 25 is a widely utilized technique for distance estimation, but its effectiveness is limited when utilizing only a monocular camera. This limitation stems from the lack of spatial disparities in images captured by a single camera, which hampers accurate distance measurement.…”

Section: Related Workmentioning

confidence: 99%

SocialVis: Dynamic social visualization in dense scenes via real‐time multi‐object tracking and proximity graph construction

Li,

Wang

et al. 2024

Computer Animation & Virtual

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To monitor and assess social dynamics and risks at large gatherings, we propose “SocialVis,” a comprehensive monitoring system based on multi‐object tracking and graph analysis techniques. Our SocialVis includes a camera detection system that operates in two modes: a real‐time mode, which enables participants to track and identify close contacts instantly, and an offline mode that allows for more comprehensive post‐event analysis. The dual functionality not only aids in preventing mass gatherings or overcrowding by enabling the issuance of alerts and recommendations to organizers, but also allows for the generation of proximity‐based graphs that map participant interactions, thereby enhancing the understanding of social dynamics and identifying potential high‐risk areas. It also provides tools for analyzing pedestrian flow statistics and visualizing paths, offering valuable insights into crowd density and interaction patterns. To enhance system performance, we designed the SocialDetect algorithm in conjunction with the BYTE tracking algorithm. This combination is specifically engineered to improve detection accuracy and minimize ID switches among tracked objects, leveraging the strengths of both algorithms. Experiments on both public and real‐world datasets validate that our SocialVis outperforms existing methods, showing improvement in detection accuracy and reduction in ID switches in dense pedestrian scenarios.

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“…The general stixel framework offers various possibilities for extensions and modifications. For instance, [14] compared the effects of different methods for initial ground manifold modeling. Driven by the requirements of autonomous driving, [15] applied a Kalman filter to track single stixels.…”

Section: Related Workmentioning

confidence: 99%

Fast and Compact Image Segmentation Using Instance Stixels

Hehn

Kooij

Gavrila

2022

IEEE Trans. Intell. Veh.

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State-of-the-art stixel methods fuse dense stereo disparity and semantic class information, e.g., from a Convolutional Neural Network (CNN), into a compact representation of driveable space, obstacles and background. However, they do not explicitly differentiate instances within the same semantic class. We investigate several ways to augment single-frame stixels with instance information, which can be extracted by a CNN from the RGB image input. As a result, our novel Instance Stixels method efficiently computes stixels that account for boundaries of individual objects, and represents instances as grouped stixels that express connectivity. Experiments on the Cityscapes dataset demonstrate that including instance information into the stixel computation itself, rather than as a post-processing step, increases the segmentation performance (i.e., Intersection over Union and Average Precision). This holds especially for overlapping objects of the same class. Furthermore, we show the superiority of our approach in terms of segmentation performance and computational efficiency compared to combining the separate outputs of Semantic Stixels and a state-of-the-art pixel-level CNN. We achieve processing throughput of 28 frames per second on average for 8 pixel wide stixels on images from the Cityscapes dataset at 1792 × 784 pixels. Our Instance Stixels software is made freely available for non-commercial research purposes.

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Effects of Ground Manifold Modeling on the Accuracy of Stixel Calculations

Cited by 10 publications

References 51 publications

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic

SocialVis: Dynamic social visualization in dense scenes via real‐time multi‐object tracking and proximity graph construction

Fast and Compact Image Segmentation Using Instance Stixels

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