A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Gochoo, Munkhjargal; Rizwan, Syeda Amna; Ghadi, Yazeed Yasin; Jalal, Ahmad; Kim, Kibum

doi:10.3390/app11125503

Cited by 25 publications

(6 citation statements)

References 54 publications

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“…behavior detection with UBI Fight dataset To evaluate the performance in more detail Error equivalence rate (EER) [44], Area under the curve (AUC) and decidability [45] are used to evaluate the performance over both datasets. AUC offers cumulative measures of performance amongst all potential classification's thresholds.…”

Section: Performance Measurement and Results Analysismentioning

confidence: 99%

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Pervaiz¹,

Alsufyani²,

Jalal³

et al. 2023

Computers, Materials &Amp; Continua

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Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian's is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian's behavior detection. Kalman filter has been used to detect pedestrian's using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian's data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.

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Section: Performance Measurement and Results Analysismentioning

confidence: 99%

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Pervaiz¹,

Alsufyani²,

Jalal³

et al. 2023

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

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“…Face recognition [7], facial expression [8], and head tracking [9] are all areas of computer vision research that have made big strides in recent years. These steps have helped image-based technologies grow quickly.…”

Section: Related Workmentioning

confidence: 99%

How to Prevent Drivers before Their Sleepiness Using Deep Learning-Based Approach

Akrout

Fakhfakh

2023

Electronics

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Drowsy driving causes many accidents. Driver alertness and automobile control are challenged. Thus, a driver drowsiness detection system is becoming a necessity. In fact, invasive approaches that analyze electroencephalography signals with head electrodes are inconvenient for drivers. Other non-invasive fatigue detection studies focus on yawning or eye blinks. The analysis of several facial components has yielded promising results, but it is not yet enough to predict hypovigilance. In this paper, we propose a “non-invasive” approach based on a deep learning model to classify vigilance into five states. The first step is using MediaPipe Face Mesh to identify the target areas. This step calculates the driver’s gaze and eye state descriptors and the 3D head position. The detection of the iris area of interest allows us to compute a normalized image to identify the state of the eyes relative to the eyelids. A transfer learning step by the MobileNetV3 model is performed on the normalized images to extract more descriptors from the driver’s eyes. Our LSTM network entries are vectors of the previously calculated features. Indeed, this type of learning allows us to determine the state of hypovigilance before it arrives by considering the previous learning steps, classifying the levels of vigilance into five categories, and alerting the driver before the state of hypovigilance’s arrival. Our experimental study shows a 98.4% satisfaction rate compared to the literature. In fact, our experimentation begins with the hyperparameter preselection to improve our results.

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“…Since the images used are taken from drones, the pedestrian targets in the images are small, and there is no occlusion problem caused by vertical and oblique viewing angles. ere are also methods based on RGBD image CNN networks [43][44][45], because this method can reduce the occlusion problem caused by oblique viewing angles, and the error rate of crowd counting is low. However, the multicolumn structure leads to difficulty in training with many redundant parameters, and although the use of ensembles of CNNs can bring significant performance improvements, they come at the cost of a large amount of computation.…”

Section: Related Workmentioning

confidence: 99%

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

Ren

2022

Scientific Programming

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Motivation. In the environment of day and night video surveillance, in order to improve the accuracy of machine vision dense crowd counting and target detection, this paper designs a day and night dual-purpose crowd counting and crowd detection network based on multimode image fusion. Methods. Two sub-models, RGBD-Net and RGBT-Net, are designed in this paper. The depth image features and thermal imaging features are effectively fused with the features of visible light images, so that the model has stronger anti-interference characteristics and robustness to the light noise interference caused by the sudden fall of light at night. The above models use density map regression-guided detection method to complete population counting and detection. Results. The model completed daytime training and testing on MICC dataset. Through verification, the average absolute error of the model was 1.025, the mean square error was 1.521, and the recall rate of target detection was 97.11%. Night vision training and testing were completed on the RGBT-CC dataset. After verification, the average absolute error of the network was 18.16, the mean square error was 32.14, and the recall rate of target detection was 97.65%. By verifying the effectiveness of the multimode medium-term fusion network, it is found to exceed the current most advanced bimodal fusion method. Conclusion. The experimental results show that the proposed multimodal fusion network can solve the counting and detection problem in the video surveillance environment during day and night. The ablation experiment further proves the effectiveness of the parameters of the two models.

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A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras

Cited by 25 publications

References 54 publications

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Tracking and Analysis of Pedestrian’s Behavior in Public Places

How to Prevent Drivers before Their Sleepiness Using Deep Learning-Based Approach

Research on 24-Hour Dense Crowd Counting and Object Detection System Based on Multimodal Image Optimization Feature Fusion

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