Counting of People in the Extremely Dense Crowd using Genetic Algorithm and Blobs Counting

Arif, Muhammad; Daud, Sultan; Basalamah, Saleh

doi:10.11591/ij-ai.v2i2.1793

Cited by 18 publications

(14 citation statements)

References 14 publications

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“…Automated analysis of crowd behaviors has large number of applications applications ranging from prediction of congestion to the discovery of abnormal behaviors or flows. Most of the research is focused on detecting anomalies in videos [7], [8], [9], [10], [11], [12], [13], counting people in crowds [14], [15], [16], [17], [18], [19], [20], [21], characterizing different motion flows and segmentation [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32]. Other works aimed at detecting/tracking individuals or group of individuals in crowd scene [33], [34], [35], [36], [37], [38], [39], [40] rather than identifying crowd behaviours.…”

Section: Related Workmentioning

confidence: 99%

Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors

Basalamah¹,

Daud²

2020

IJACSA

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Crowd analysis is receiving much attention from research community due to its widespread importance in public safety and security. In order to automatically understand crowd dynamics, it is imperative to detect and segment crowd from the background. Crowd detection and segmentation serve as preprocessing step in most crowd analysis applications, for example, crowd tracking, behavior understanding and anomaly detection. Intuitively, the crowd regions can be extracted using background modeling or using motion cues. However, these model accumulate many false positives when the crowd is static. In this paper, we propose a novel framework that automatically detects and segments crowd by integrating appearance features from multiple sources. We evaluate our proposed framework using challenging images with varying crowd densities, camera viewpoints and pedestrian appearances. From qualitative analysis, we observe that the proposed framework work perform well by precisely segmenting crowd in complex scenes.

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Section: Related Workmentioning

confidence: 99%

Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors

Basalamah¹,

Daud²

2020

IJACSA

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“…For the video surveillance and smart rehabilitation, it is important to observe and monitor human actions for a long period [17], [18]. It is humanly impossible to monitor these actions for long duration due to limited human capabilities [19], [20]. Therefore, there is an increased interest to automate this process by using surveillance camera installed in different location of scene.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Human Behaviours Using Statistical Motion Descriptor

Alreshidi¹,

Bilal²

2019

SIPIJ

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Identifying human behaviors is a challenging research problem due to the complexity and variation of appearances and postures, the variation of camera settings, and view angles. In this paper, we try to address the problem of human behavior identification by introducing a novel motion descriptor based on statistical features. The method first divide the video into N number of temporal segments. Then for each segment, we compute dense optical flow, which provides instantaneous velocity information for all the pixels. We then compute Histogram of Optical Flow (HOOF) weighted by the norm and quantized into 32 bins. We then compute statistical features from the obtained HOOF forming a descriptor vector of 192dimensions. We then train a non-linear multi-class SVM that classify different human behaviors with the accuracy of 72.1%. We evaluate our method by using publicly available human action data set. Experimental results shows that our proposed method out performs state of the art methods.

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“…It means that the motion information does not take into account individual moving or static objects [19][20 [21]. These methods therefore, often need various features including multi-resolution histograms [22][23], spatio-temporal cuboids [24] [25], appearance or motion descriptors [26] [27] and spatio-temporal cubes [28] [29].…”

Section: Introductionmentioning

confidence: 99%

Challenges of Deep Learning for Crowd Analytics

Siraj¹

2019

Preprint

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In high population cities, the gatherings of large crowds in public places and public areas accelerate or jeopardize people safety and transportation, which is a key challenge to the researchers. Although much research has been carried out on crowd analytics, many of existing methods are problem-specific, i.e., methods learned from a specific scene cannot be properly adopted to other videos. Therefore, this presents weakness and the discovery of these researches, since additional training samples have to be found from diverse videos. This paper will investigate diverse scene crowd analytics with traditional and deep learning models. We will also consider pros and cons of these approaches. However, once general deep methods are investigated from large datasets, they can be consider to investigate different crowd videos and images. Therefore, it would be able to cope with the problem including to not limited to crowd density estimation, crowd people counting, and crowd event recognition. Deep learning models and approaches are required to have large datasets for training and testing. Many datasets are collected taking into account many different and various problems related to building crowd datasets, including manual annotations and increasing diversity of videos and images. In this paper, we will also propose many models of deep neural networks and training approaches to learn the feature modeling for crowd analytics.

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Counting of People in the Extremely Dense Crowd using Genetic Algorithm and Blobs Counting

Cited by 18 publications

References 14 publications

Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors

Pedestrian Crowd Detection and Segmentation using Multi-Source Feature Descriptors

Characterizing Human Behaviours Using Statistical Motion Descriptor

Challenges of Deep Learning for Crowd Analytics

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