14,657 research outputs found
PETS2009 and Winter-PETS 2009 results: a combined evaluation
This paper presents the results of the crowd image analysis
challenge of the Winter PETS 2009 workshop. The evaluation
is carried out using a selection of the metrics developed
in the Video Analysis and Content Extraction (VACE)
program and the CLassification of Events, Activities, and
Relationships (CLEAR) consortium [13]. The evaluation
highlights the detection and tracking performance of the authors’systems in areas such as precision, accuracy and robustness. The performance is also compared to the PETS
2009 submitted results
Online real-time crowd behavior detection in video sequences
Automatically detecting events in crowded scenes is a challenging task in Computer Vision. A number of offline approaches have been proposed for solving the problem of crowd behavior detection, however the offline assumption limits their application in real-world video surveillance systems. In this paper, we propose an online and real-time method for detecting events in crowded video sequences. The proposed approach is based on the combination of visual feature extraction and image segmentation and it works without the need of a training phase. A quantitative experimental evaluation has been carried out on multiple publicly available video sequences, containing data from various crowd scenarios and different types of events, to demonstrate the effectiveness of the approach
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation
In real-world crowd counting applications, the crowd densities vary greatly
in spatial and temporal domains. A detection based counting method will
estimate crowds accurately in low density scenes, while its reliability in
congested areas is downgraded. A regression based approach, on the other hand,
captures the general density information in crowded regions. Without knowing
the location of each person, it tends to overestimate the count in low density
areas. Thus, exclusively using either one of them is not sufficient to handle
all kinds of scenes with varying densities. To address this issue, a novel
end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density
Estimation Network) is proposed. It can adaptively decide the appropriate
counting mode for different locations on the image based on its real density
conditions. DecideNet starts with estimating the crowd density by generating
detection and regression based density maps separately. To capture inevitable
variation in densities, it incorporates an attention module, meant to
adaptively assess the reliability of the two types of estimations. The final
crowd counts are obtained with the guidance of the attention module to adopt
suitable estimations from the two kinds of density maps. Experimental results
show that our method achieves state-of-the-art performance on three challenging
crowd counting datasets.Comment: CVPR 201
Towards Smarter Management of Overtourism in Historic Centres Through Visitor-Flow Monitoring
Historic centres are highly regarded destinations for watching and even participating in diverse and unique forms of cultural expression. Cultural tourism, according to the World Tourism Organization (UNWTO), is an important and consolidated tourism sector and its strong growth is expected to continue over the coming years. Tourism, the much dreamt of redeemer for historic centres, also represents one of the main threats to heritage conservation: visitors can dynamize an economy, yet the rapid growth of tourism often has negative effects on both built heritage and the lives of local inhabitants. Knowledge of occupancy levels and flows of visiting tourists is key to the efficient management of tourism; the new technologies—the Internet of Things (IoT), big data, and geographic information systems (GIS)—when combined in interconnected networks represent a qualitative leap forward, compared to traditional methods of estimating locations and flows. A methodology is described in this paper for the management of tourism flows that is designed to promote sustainable tourism in historic centres through intelligent support mechanisms. As part of the Smart Heritage City (SHCITY) project, a collection system for visitors is developed. Following data collection via monitoring equipment, the analysis of a set of quantitative indicators yields information that can then be used to analyse visitor flows; enabling city managers to make management decisions when the tourism-carrying capacity is exceeded and gives way to overtourism.Funded by the Interreg Sudoe Programme of the European Regional Development Funds (ERDF
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