Benchmark Data and Method for Real-Time People Counting in Cluttered Scenes Using Depth Sensors

Sun, Shijie; Akhtar, Naveed; Song, Huansheng; Zhang, Chaoyang; Li, Jianxin; Mian, Ajmal

doi:10.1109/tits.2019.2911128

Cited by 58 publications

(36 citation statements)

References 54 publications

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“…In a public transport system, the available seating capacity is a concern for commuters who depend on such mode of transport every day. During peak hours, commuters may prefer to use taxis rather than overcrowded buses/subways [21,39]. A smarter decision of whether to use public transport can be made if the traveler has access to information on the available seating capacity at a time and the chance of getting a seat for bus/subway for taking the journey.…”

Section: Seating Availability In Public Transportmentioning

confidence: 99%

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

Asaithambi

Venkatraman

Venkatraman³

2020

BDCC

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Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

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Section: Seating Availability In Public Transportmentioning

confidence: 99%

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

Asaithambi

Venkatraman

Venkatraman³

2020

BDCC

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“…It measures the minimum number of steps between the central player and other players as a supplement to the importance of players [18]. Reference [20] discussed the impact of real-time personnel gathering changes. On the macrolevel, with the use of coordination, adaptability, and flexibility as a quantitative indicator of team performance, rhythm is better than the average number of team passes based on SNA ideas [21,22].…”

Section: Performance Analysis Based On Passing Networkmentioning

confidence: 99%

Efficient Strategy Mining for Football Social Network

et al. 2020

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With the growing popularity of social network in sport, it expresses the social relationships between individuals and facilitates realistic applications, e.g., social event mining and discovery. Sport network as a specific social network has been widely studied in research and commercial fields. However, most of the existing works utilize a simplex strategy to improve certain indicators in the team and do not consider the effect of strategy adjustment based on the current situation. In this paper, we study the problem of efficient strategy mining in football social network. To address this problem, we propose a quantitative way to combine the aspects of coordination, adaptability, flexibility, and tempo into a passing network, which notably improves the timeliness and information content of the existing network. On this basis, we design a suppression function to express the impact of strategy. Then, we propose a novel passing network and group cooperation scheme based on quantified team performance to obtain the efficient strategies. At last, the experimental results show that, based on the performance of the same team, our optimized passing network has a higher winning rate in practice.

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“…RGBD-based human detection methods generally utilize the depth information provided by depth images in combination with the color information provided by RGB images to perform multimodal feature fusion or joint decision making. Sun et al [ 21 ] first extracted human heads from the projection depth image for human detection. Huang et al [ 22 ] extracted histogram of orientation gradient (HOG) features and Haar-like features from color images and depth images, respectively.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection

Zhang

Guo

Wang

et al. 2021

Sensors

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In recent years, human detection in indoor scenes has been widely applied in smart buildings and smart security, but many related challenges can still be difficult to address, such as frequent occlusion, low illumination and multiple poses. This paper proposes an asymmetric adaptive fusion two-stream network (AAFTS-net) for RGB-D human detection. This network can fully extract person-specific depth features and RGB features while reducing the typical complexity of a two-stream network. A depth feature pyramid is constructed by combining contextual information, with the motivation of combining multiscale depth features to improve the adaptability for targets of different sizes. An adaptive channel weighting (ACW) module weights the RGB-D feature channels to achieve efficient feature selection and information complementation. This paper also introduces a novel RGB-D dataset for human detection called RGBD-human, on which we verify the performance of the proposed algorithm. The experimental results show that AAFTS-net outperforms existing state-of-the-art methods and can maintain stable performance under conditions of frequent occlusion, low illumination and multiple poses.

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Benchmark Data and Method for Real-Time People Counting in Cluttered Scenes Using Depth Sensors

Cited by 58 publications

References 54 publications

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

Efficient Strategy Mining for Football Social Network

Asymmetric Adaptive Fusion in a Two-Stream Network for RGB-D Human Detection

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