A Novel Crowd Density Estimation Technique using Local Binary Pattern and Gabor Features

Pai, Abhilash K.; Karunakar, A. K.; Raghavendra, U.

doi:10.1109/avss.2017.8078556

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Since detection performance can be severely affected in overcrowded real-time scenes, detection-based methods are often outperformed by density map regression-based methods. The success of density map-based regression methods can be attributed to their ability to bypass explicit detection and map input images directly to scalar values [49][50][51]. However, although the method based on density regression can perceive the distribution of the crowd, it loses the ability to generate the individual localization of the crowd, so it is difficult to further study the dense crowd tracking and reidentification technology in surveillance.…”

Section: Related Workmentioning

confidence: 99%

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Ren

2022

Journal of Sensors

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Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.

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Section: Related Workmentioning

confidence: 99%

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Ren

2022

Journal of Sensors

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“…erefore, the counting problem of visually indistinguishable crowded images cannot be completely solved. [8,[43][44][45][46][47][48][49]. Powerful CNNs play an important role in the density map regression process, and Wang et al [43] show that features extracted from deep models are more effective than handcrafted features.…”

Section: Related Workmentioning

confidence: 99%

“…Pai [ 44 ] et al aim to achieve dense crowd counting in visually indistinguishable crowded images. This method convolves image patches with a Gabor filter and classifies the responses of the Gabor filter with a support vector machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

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Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

Ren

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Background. In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods. In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN “mid-term” fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results. MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion. MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.

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“…Intelligent video surveillance is a vintage subject in the domain of image processing and computer vision that has recently become well known. It has numerous significant benefits, such as accurate data processing, low human resource cost, and effective information gathering organization [4]. Crowd density estimation is regarded as a significant application in visual surveillance, and it plays an important role in crowd management and monitoring.…”

Section: Introductionmentioning

confidence: 99%

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

et al. 2022

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Object detection is a computer vision based technique which is used to detect instances of semantic objects of a particular class in digital images and videos. Crowd density analysis is one of the commonly utilized applications of object detection. Since crowd density classification techniques face challenges like non-uniform density, occlusion, inter-scene, and intra-scene deviations, convolutional neural network (CNN) models are useful. This paper presents a Metaheuristics with Deep Transfer Learning Enabled Intelligent Crowd Density Detection and Classification (MDTL-ICDDC) model for video surveillance systems. The proposed MDTL-ICDDC technique mostly concentrates on the effective identification and classification of crowd density on video surveillance systems. In order to achieve this, the MDTL-ICDDC model primarily leverages a Salp Swarm Algorithm (SSA) with NASNetLarge model as a feature extraction in which the hyperparameter tuning process is performed by the SSA. Furthermore, a weighted extreme learning machine (WELM) method was utilized for crowd density and classification process. Finally, the krill swarm algorithm (KSA) is applied for an effective parameter optimization process and thereby improves the classification results. The experimental validation of the MDTL-ICDDC approach was carried out with a benchmark dataset, and the outcomes are examined under several aspects. The experimental values indicated that the MDTL-ICDDC system has accomplished enhanced performance over other models such as Gabor, BoW-SRP, Bow-LBP, GLCM-SVM, GoogleNet, and VGGNet.

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A Novel Crowd Density Estimation Technique using Local Binary Pattern and Gabor Features

Cited by 12 publications

References 18 publications

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask

Deep Transfer Learning Enabled Intelligent Object Detection for Crowd Density Analysis on Video Surveillance Systems

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