Crowd Classification Research Articles

Crowd-Driven Deep Learning Tracks Amazon Deforestation

The Amazon forests act as a global reserve for carbon, have very high biodiversity, and provide a variety of additional ecosystem services. These forests are, however, under increasing pressure, coming mainly from deforestation, despite the fact that accurate satellite monitoring is in place that produces annual deforestation maps and timely alerts. Here, we present a proof of concept for rapid deforestation monitoring that engages the global community directly in the monitoring process via crowdsourcing while subsequently leveraging the power of deep learning. Offering no tangible incentives, we were able to sustain participation from more than 5500 active contributors from 96 different nations over a 6-month period, resulting in the crowd classification of 43,108 satellite images (representing around 390,000 km2). Training a suite of AI models with results from the crowd, we achieved an accuracy greater than 90% in detecting new and existing deforestation. These findings demonstrate the potential of a crowd–AI approach to rapidly detect and validate deforestation events. Our method directly engages a large, enthusiastic, and increasingly digital global community who wish to participate in the stewardship of the global environment. Coupled with existing monitoring systems, this approach could offer an additional means of verification, increasing confidence in global deforestation monitoring.

A Comparative Study of MobileNet Architecture Optimizer for Crowd Prediction

Artificial intelligence technology has grown quickly in recent years. Convolutional neural network (CNN) technology has also been developed as a result of these developments. However, because convolutional neural networks entail several calculations and the optimization of numerous matrices, their application necessitates the utilization of appropriate technology, such as GPUs or other accelerators. Applying transfer learning techniques is one way to get around this resource barrier. MobileNetV2 is an example of a lightweight convolutional neural network architecture that is appropriate for transfer learning. The objective of the research is to compare the performance of SGD and Adam using the MobileNetv2 convolutional neural network architecture. Model training uses a learning rate of 0.0001, batch size of 32, and binary cross-entropy as the loss function. The training process is carried out for 100 epochs with the application of early stop and patience for 10 epochs. Result of this research is both models using Adam's optimizer and SGD show good capability in crowd classification. However, the model with the SGD optimizer has a slightly superior performance even with less accuracy than model with Adam optimizer. Which is model with Adam has accuracy 96%, while the model with SGD has 95% accuracy. This is because in the graphical results model with the SGD optimizer shows better stability than the model with the Adam optimizer. The loss graph and accuracy graph of the SGD model are more consistent and tend to experience lower fluctuations than the Adam model.

Racism Dissemination Model and Simulation Analysis Considering Crowd Classification with Intervention Strategies

The extensive propagation of racism and the endless occurrence of racist speeches on social outlets imperil the harmony and resilience of societies. Consecutively, it is required to work in-depth analysis of the structure process, dynamical interaction, and intervention impact on racism spread and related devastation. As such, the diffusion of racism may flow via a socially contagious approach, in which racist ideologies bear like an infectious disease transmission. In this study, we propose and analyze the S 1 S 2 S 3 H R C I compartmental mathematical model to comprehend how racists disseminate their opinions and influence society with intervention strategies. We proved the well-posedness of the proposed model. Convection, hesitation, and rejection of racist ideology measure the strength of racist militancy. Through rigorous theoretical analysis, racism-free and racism-addicted equilibrium are locally and globally asymptotically stable if the effective threshold value R e < 1 and R e > 1 , respectively. Sensitivity analyses are performed based on the effective threshold value to seek influential control parameters of the model. The result implies high levels of racist combativeness and low likelihood of rejection of awarded individuals have fostered racism dissemination. Nevertheless, effective antiracism education and racist confinement create a social environment beyond racism and reduce the peril of bigotry. Through extensive simulations, we study the racism dissemination process will decline and vanish if the community resilience boots via antiracism education and efficient racist confinement. Furthermore, racist confinement is more effective when compared to antiracism education. We examine the impact of integrating interventions, and the result exhibits that combining intervention strategies is better relevant than a sole intervention.

Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification.

The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks.

Sparrow Search Optimization with Transfer Learning-Based Crowd Density Classification

Due to the rapid increase in urbanization and population, crowd gatherings are frequently observed in the form of concerts, political, and religious meetings. HAJJ is one of the well-known crowding events that takes place every year in Makkah, Saudi Arabia. Crowd density estimation and crowd monitoring are significant research areas in Artificial Intelligence (AI) applications. The current research study develops a new Sparrow Search Optimization with Deep Transfer Learning based Crowd Density Detection and Classification (SSODTL-CD2C) model. The presented SSODTL-CD2C technique majorly focuses on the identification and classification of crowd densities. To attain this, SSODTL-CD2C technique exploits Oppositional Salp Swarm Optimization Algorithm (OSSA) with EfficientNet model to derive the feature vectors. At the same time, Stacked Sparse Auto Encoder (SSAE) model is utilized for the classification of crowd densities. Finally, SSO algorithm is employed for optimal fine-tuning of the parameters involved in SSAE mechanism. The performance of the proposed SSODTL-CD2C technique was validated using a dataset with four different kinds of crowd densities. The obtained results demonstrated that the proposed SSODTL-CD2C methodology accomplished an excellent crowd classification performance with a maximum accuracy of 93.25%. So, the proposed method will be highly helpful in managing HAJJ and other crowded events.

An effective online platform for crowd classification of coastal wetland loss

AbstractWetland loss is increasing rapidly, and there are gaps in public awareness of the problem. By crowdsourcing image analysis of wetland morphology, academic and government studies could be supplemented and accelerated while engaging and educating the public. The Land Loss Lookout (LLL) project crowdsourced mapping of wetland morphology associated with wetland loss and restoration. We demonstrate that volunteers can be trained relatively easily online to identify characteristic wetland morphologies, or patterns present on the landscape that suggest a specific geomorphological process. Results from a case study in coastal Louisiana revealed strong agreement among nonexpert and expert assessments who agreed on classifications at least 83% and at most 94% of the time. Participants self‐reported increased knowledge of wetland loss after participating in the project. Crowd‐identified morphologies are consistent with expectations, although more work is needed to directly compare LLL results with previous studies. This work provides a foundation for using crowd‐based wetland loss analysis to increase public awareness of the issue, and to contribute to land surveys or train machine learning algorithms.

Motion pattern-based crowd scene classification using histogram of angular deviations of trajectories

Automated crowd behaviour analysis and monitoring is a challenging task due to the unpredictable nature of the crowd within a particular scene and across different scenes. The prior knowledge of the type of scene under consideration is a crucial mid-level information, which could be utilized to develop robust crowd behaviour analysis systems. In this paper, we propose an approach to automatically detect the type of a crowded scene based on the global motion patterns of the objects within the scene. Three different types of scenes whose global motion pattern characteristics vary from uniform to non-uniform are considered in this work, namely structured, semi-structured, and unstructured scenes, respectively. To capture the global motion pattern characteristics of an input crowd scene, we first extract the motion information in the form of trajectories using a key-point tracker and then compute the average angular orientation feature of each trajectory. This paper utilizes these angular features to introduce a novel feature vector, termed as Histogram of Angular Deviations (HAD), which depicts the distribution of the pair-wise angular deviation values for each trajectory vector. Since angular deviation information is resistant to changes in scene perspectives, we consider it as a key feature for distinguishing the scene types. To evaluate the effectiveness of the proposed HAD-based feature vector in classifying the crowded scenes, we build a crowd scene classification model by training the classical machine learning algorithms on the publicly available Collective Motion Database. The experimental results demonstrate the superior crowd classification performance of the proposed approach as compared to the existing methods. In addition to this, we propose a technique based on quantizing the angular deviation values to reduce the feature dimension and subsequently introduce a novel crowd scene structuredness index to quantify the structuredness of an input crowded scene based on its HAD.

The Crowd Classification Problem: Social Dynamics of Binary-Choice Accuracy

Decades of research suggest that information exchange in groups and organizations can reliably improve judgment accuracy in tasks such as financial forecasting, market research, and medical decision making. However, we show that improving the accuracy of numeric estimates does not necessarily improve the accuracy of decisions. For binary-choice judgments, also known as classification tasks—for example, yes/no or build/buy decisions—social influence is most likely to grow the majority vote share, regardless of the accuracy of that opinion. As a result, initially, inaccurate groups become increasingly inaccurate after information exchange, even as they signal stronger support. We term this dynamic the “crowd classification problem.” Using both a novel data set and a reanalysis of three previous data sets, we study this process in two types of information exchange: (1) when people share votes only, and (2) when people form and exchange numeric estimates prior to voting. Surprisingly, when people exchange numeric estimates prior to voting, the binary-choice vote can become less accurate, even as the average numeric estimate becomes more accurate. Our findings recommend against voting as a form of decision making when groups are optimizing for accuracy. For those cases where voting is required, we discuss strategies for managing communication to avoid the crowd classification problem. We close with a discussion of how our results contribute to a broader contingency theory of collective intelligence. This paper was accepted by Lamar Pierce, organizations.

Crowd Sourcing: Do Peer Crowd Prototypes Match Reality?

During the transition into high school, adolescents sort large sets of unfamiliar peers into prototypical peer crowds thought to share similar values, behaviors, and interests (e.g., Jocks). Often, such sorting is based solely on appearance. This study investigates the accuracy of this sorting process in relation to actual characteristics using video and survey data from a longitudinal sample of U.S. youths who attended high school in the mid- to late-2000s. To simulate this sorting process, we asked same-birth-cohort strangers to view short videos of youths at age 15 and to classify those strangers into likely crowd membership. We then compared the classifications they made to how adolescents characterized themselves at that same time point. Results show that peer crowd classification predicts aspects of unknown peers' mental health, academic achievement, extracurricular involvement, social status, and risk-taking behaviors.

Rumor spreading model considering rumor credibility, correlation and crowd classification based on personality

The study of rumor spreading or rumor controlling is important and necessary because rumors can cause serious negative effects on society. The process of rumor spreading is influenced by many factors. In this paper, we suggest that people with different personalities will behave differently after hearing rumors. Thus, we divide the population into two types: radical people and steady people. Furthermore, we suggest that the credibility of rumors and the correlation between rumors and people’s lives are important factors that will influence the spread of rumors. Based on these considerations, we propose the SEIsIrR model. We establish differential equations to describe the dynamics of the rumor spreading process in homogeneous and heterogeneous networks. Using the Jacobian matrix and next generation matrix, we obtain the spreading threshold of the SEIsIrR model and discuss the relationship of the spreading threshold between homogeneous networks and heterogeneous networks. We employ a real rumor dataset obtained from Twitter to verify the SEIsIrR model and perform numerical simulations in Watts-Strogatz (WS) networks and Barabasi-Albert (BA) networks to verify the obtained spreading thresholds and discuss the impacts of these factors on the rumor spreading process and the differences in the rumor spreading processes between WS networks and BA networks. The simulation results show that these factors influence the speed and range of rumor spreading.

LCrowdV: Generating labeled videos for pedestrian detectors training and crowd behavior learning

We present a novel procedural framework to generate an arbitrary number of labeled crowd videos (LCrowdV). The resulting crowd video datasets are used to design accurate algorithms or training models for crowded scene understanding. Our overall approach is composed of two components: a procedural simulation framework to generate crowd movements and behaviors, and a procedural rendering framework to generate different videos or images. Each video or image is automatically labeled based on the environment, number of pedestrians, density, behavior (personality), flow, lighting conditions, viewpoint, type of noise, etc. Furthermore, we can increase the realism by combining synthetically-generated behaviors with real-world background videos. We demonstrate the benefits of LCrowdV over prior lableled crowd datasets by augmenting a real dataset with it and improving the accuracy in pedestrian detection and crowd classification. Furthermore, we evaluate the impact of removing the variety in different LCrowdV parameters to show the importance of the diversity of data generated from our framework. LCrowdV has been made available as an online resource.

THE ANALYSIS ON THE STAKEHOLDERS OF CONFLICT MITIGATION IN THE WAY KAMBAS NATIONAL PARK, LAMPUNG

Stakeholders with different interests and influences (power) are related to the human-wild elephant conflict mitigation in the Way Kambas National Park in Lampung. Stakeholders need to manage properly to achieve conflict mitigation objectives optimally. This research was conducted in Way Kambas National Park in Lampung. Data were collected with observations, interviews, and related document studies without involving active participations of related stakeholders and data were analyzed by using descriptive qualitative analysis. This research was conducted from September to December 2016. The identification results showed that there were 11 stakeholders dealing with the human-wild elephant conflict mitigation in Way Kambas National Park. The identified stakeholders were divided into three classifications: (1) subject stakeholders coming from farmer groups and people from buffering villages, (2) key player stakeholders consisting of Way Kambas National Park Institution, regional government (village, district and provincial governments), local partners/non-government organizations (Sumatra Forest Conservation and Wildlife Conservation Society-Indonesia Program), Forum Rembug Desa Penyangga/village discussion forums, and Lampung University, and (3) the context setter stakeholder from a private company (PT. Nusantara Tropical Fruit). This research did not find stakeholder from crowd classification. The relationships between stakeholders showed complementary cooperation. The implication of human-wild elephant conflict mitigation management in Way Kambas National Park is that the stakeholder management to build cooperation and collaboration in the form of communication forums to achieve the objectives of human-wild elephant conflict mitigation in Way Kambas National Park is required.Keywords: stakeholder, power-interest matrix, actor-linkage matrix, Way Kambas National Park

HSBS: A Human’s Heat Signature and Background Subtraction Hybrid Approach for Crowd Counting and Analysis

This work presents a new approach for crowd counting and classification based upon human thermal and motion features. The technique is efficient for automatic crowd density estimation and type of motion determination. Crowd density is measured without any need for camera calibration or assumption of prior knowledge about the input videos. It does not need any human intervention so it can be used successfully in a fully automated crowd control systems. Two new features are introduced for crowd counting purpose: the first represents thermal characteristics of humans and is expressed by the ratio between their temperature and their ambient environment temperature. The second describes humans motion characteristics and is measured by the ratio between humans motion velocity and the ambient environment rigidity. Each ratio should exceed a certain predetermined threshold for human beings. These features have been investigated and proved to give accurate crowd counting performance in real time. Moreover, the two features are combined and used together for crowd classification into one of the three main types, which are: fully mobile, fully static, or mix of both types. Last but not least, the proposed system offers several advantages such as being a privacy preserving crowd counting system, reliable for homogeneous and inhomogeneous crowds, does not depend on a certain direction in motion detection, has no restriction on crowd size. The experimental results demonstrate the effectiveness of the approach.

Multi-Object Classification via Crowdsourcing With a Reject Option

Consider designing an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final result. We consider the novel scenario where workers have a reject option so they may skip microtasks when they are unable or choose not to respond. For example, in mismatched speech transcription, workers who do not know the language may not be able to respond to microtasks focused on phonological dimensions outside their categorical perception. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize the crowd's classification performance. We evaluate system performance in both exact and asymptotic forms. Further, we consider the setting where there may be a set of greedy workers that complete microtasks even when they are unable to perform it reliably. We consider an oblivious and an expurgation strategy to deal with greedy workers, developing an algorithm to adaptively switch between the two based on the estimated fraction of greedy workers in the anonymous crowd. Simulation results show improved performance compared with conventional majority voting.

Effective Approaches to Control the Epidemic Based on the Improved SIR Model

<p class="zhengwen">In this paper, we have established a SECADI model on the basis of the traditional epidemic model and under the consideration of factors such as the spread of the disease, the quantity of the medicine in need, the medicine production speedetc. We have improved the crowd classification standard and the spread styledifferential equation model in classical SIR model. We distinguished the crowd into six categories, including the susceptible, the exposed, the curable, the advanced, the dead and the immune, and we established integrated transformation relationships between them after taking control measures through qualitative and quantitative method, and then derive the adequate epidemic differential equation model before taking controls. We applied the method of computer simulation to solve the model, worked out uncertain parameters with the method of parameter identification, and we verified the validity and accuracy of the SECADI model. Meanwhile, we calculated with the actual data of Ebola in the epidemic area in WesternAfrica, simulated the evolution of the epidemic, analyze and offered effective approaches to control the epidemic situation. We further discussed development directions of this model in the end.</p>