Pedestrian monitoring techniques for crowd-flow prediction

Albattah

et al. 2020

Sensors

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

Section: Applicationsmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Albattah

et al. 2020

Sensors

“…In the recent past, huge numbers of people have died from suffocation in highly crowded areas in different public-gathering events. Early detection of overcrowding and better crowd management in political rallies, sports events, and musical concerts can be made possible by analyzing the crowd gathering [129][130][131].…”

Section: Applications Of Cnn-cc Algorithmsmentioning

confidence: 99%

Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation

Ilyas

Shahzad

Kim

2019

Sensors

Traditional handcrafted crowd-counting techniques in an image are currently transformed via machine-learning and artificial-intelligence techniques into intelligent crowd-counting techniques. This paradigm shift offers many advanced features in terms of adaptive monitoring and the control of dynamic crowd gatherings. Adaptive monitoring, identification/recognition, and the management of diverse crowd gatherings can improve many crowd-management-related tasks in terms of efficiency, capacity, reliability, and safety. Despite many challenges, such as occlusion, clutter, and irregular object distribution and nonuniform object scale, convolutional neural networks are a promising technology for intelligent image crowd counting and analysis. In this article, we review, categorize, analyze (limitations and distinctive features), and provide a detailed performance evaluation of the latest convolutional-neural-network-based crowd-counting techniques. We also highlight the potential applications of convolutional-neural-network-based crowd-counting techniques. Finally, we conclude this article by presenting our key observations, providing strong foundation for future research directions while designing convolutional-neural-network-based crowd-counting techniques. Further, the article discusses new advancements toward understanding crowd counting in smart cities using the Internet of Things (IoT).

“…Based on the extracted features, the model was designed to select the optimal route for the passengers' target station. In a comparative study on two pedestrian monitoring techniques to predict crowd flow, Martani et al [47] compared an array of infrared depth sensors and a visible light (RGB) camera. Their findings revealed that the developed RGB-based system performed reliably across a wide range of conditions, while the former approach was demonstrated to be a useful supplement in conditions without significant ambient sunlight, such as underground passageways.…”

Section: Crowd Spatio-temporal Analysismentioning

confidence: 99%

Comparison of Main Approaches for Extracting Behavior Features from Crowd Flow Analysis

Ebrahimpour

Wan

Cervantes

et al. 2019

IJGI

Extracting features from crowd flow analysis has become an important research challenge due to its social cost and the impact of inadequate planning of high-quality services and security monitoring on the lives of citizens. This paper descriptively reviews and compares existing crowd analysis approaches based on different data sources. This survey provides the fundamentals of crowd analysis and considers three main approaches: crowd video analysis, crowd spatio-temporal analysis, and crowd social media analysis. The key research contributions in each approach are presented, and the most significant techniques and algorithms used to improve the precision of results that could be integrated into solutions to enhance the quality of services in a smart city are analyzed.