Survey of Crowd Detection Algorithms using Wireless Sensor Networks: A Case of People Crowds

Abstract

In this paper a survey is carried out on crowd detection algorithms, highlighting challenges and gaps visa -avis people crowd detection. The research identifies some of the key capabilities considered invaluable in effective people crowd detection and compares some of the current crowd detection algorithms with regard to these. The results reveal that most algorithms are primarily non-people crowd detection algorithms. While people crowds are intelligent and can easily bypass most of the current crowd detection algorithms. Given the need for detection and management of people crowds, there is great need for specialized algorithms for people crowd detection using effective and non resource intensive methods .

A General Purpose Data and Query Privacy Preserving Protocol for Wireless Sensor Networks

“…Active attacks are the ones in which the attacker tries to interfere with the normal functioning of the WSN. Besides building monitoring, the conceptualized technique can be extended to other scenarios, such as fall prediction in health monitoring using smart floor [62] or crowd monitoring and detection using WSN [63]. We point out a possible application in networks of untrusted devices, in which nodes of the network must jointly compute a function without knowing other parties involved in the computation and without revealing their private inputs.…”

A Survey on Regression-Based Crowd Counting Techniques

“…Crowd counting techniques are essential to ensure the public safety, such as preventing stampede in a parade, or optimizing layout of the site. To estimate the number of people within a district, a common strate-gy is to count the number of mobile phones accessed to the base station [1]. This strategy is generally effective, but it cannot reveal the local crowd density in the certain area with high risk, such as crossroad and pla-Information Technology and Control 2023/3/52 694 za.…”

“…For each image, all pedestrians' heads are manually annotated with crosses as ground truth. A common approach to generate ground-truth density map D GT , is to calculate the convolution of spatial information and a gaussian kernel G, which can be expressed as Equation (1).…”

“…Thus, research have been made to optimize the feature extraction process with various tactics. (1) The first encountered challenge is the pedestrians' heads often have different sizes within a footage since the existing of perspective. To extract the most appropriate feature with CNN-based network, multiple kernels with various scales are adapted.…”

“…3 Modeling the loss function: A proper loss function can update network parameters more effectively in back-propagation process and generate better model. (1) The most common way is to adapt L 1 or L 2 norm for either global or local loss. However, due to the uneven distribution of the crowd, these ordinary losses cannot well-handle the area with the highest density.…”