A fast background subtraction method using kernel density estimation for people counting

Cao, Jianzhao; Victor, Osuji Chukwunonso; Gilbert, Odoom Manfred; Wang, Changtao

doi:10.1109/icmic.2017.8321626

Cited by 4 publications

(5 citation statements)

References 10 publications

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“…This type of HOG detection method uses two descriptors of the same size which use the symmetry and continuity of shoulder contours to get higher discrimination and greater detection accuracy. d i and d j are the two descriptors, the sample p joint HOG features can be calculated as [46];…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…We extracted a 1D center gradient operator [−1, 0]. The horizontal gradient and the vertical gradient are calculated as [46];…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…whereas, the pixel gradients located at position (x,y) are calculated as [46]; hg(x, y) = hg x (x, y) 2 + hg y (x, y) 2 ( 7)…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…The directional range is from 0-180 • for the joint HOG features of the shoulders, with 5 bin directions. The joint HOG features for the shoulders are then expressed as [46];…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…After the normalization of all the HOG features to obtain joint HOG features of different blocks (See Section 3.2), histograms for all the overlapping blocks are collected over the detection window [46]. Then, detection window values are fused with Local Binary Pattern (LBP) to improve the performance of feature extraction.…”

Section: Fused Joint Hog-lbpmentioning

confidence: 99%

See 4 more Smart Citations

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

et al. 2021

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Automatic head tracking and counting using depth imagery has various practical applications in security, logistics, queue management, space utilization and visitor counting. However, no currently available system can clearly distinguish between a human head and other objects in order to track and count people accurately. For this reason, we propose a novel system that can track people by monitoring their heads and shoulders in complex environments and also count the number of people entering and exiting the scene. Our system is split into six phases; at first, preprocessing is done by converting videos of a scene into frames and removing the background from the video frames. Second, heads are detected using Hough Circular Gradient Transform, and shoulders are detected by HOG based symmetry methods. Third, three robust features, namely, fused joint HOG-LBP, Energy based Point clouds and Fused intra-inter trajectories are extracted. Fourth, the Apriori-Association is implemented to select the best features. Fifth, deep learning is used for accurate people tracking. Finally, heads are counted using Cross-line judgment. The system was tested on three benchmark datasets: the PCDS dataset, the MICC people counting dataset and the GOTPD dataset and counting accuracy of 98.40%, 98%, and 99% respectively was achieved. Our system obtained remarkable results.

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Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…We extracted a 1D center gradient operator [−1, 0]. The horizontal gradient and the vertical gradient are calculated as [46];…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…whereas, the pixel gradients located at position (x,y) are calculated as [46]; hg(x, y) = hg x (x, y) 2 + hg y (x, y) 2 ( 7)…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

“…The directional range is from 0-180 • for the joint HOG features of the shoulders, with 5 bin directions. The joint HOG features for the shoulders are then expressed as [46];…”

Section: Head and Shoulders Detectionmentioning

confidence: 99%

Section: Fused Joint Hog-lbpmentioning

confidence: 99%

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

et al. 2021

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Research on Foreground Object Recognition Tracking and Background Restoration in AIoT Era

Peng

et al. 2021

2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC)

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Counting Individuals in an Image using Machine Learning Technique

D P,

Kumar

2024

International Journal of Innovative Science and Research Techno

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In video surveillance system, the most complex thing is to detect individuals. In recent years, the research is done using deep learning technique, which gives powerful individuals detection results. A model in Deep Learning i.e. YOLO (You Only Look Once) has explored in individual detection in all the angles of a individuals. The model is tested and trained on viewing person dataset. Further, counting individuals has done through information of classified bounding box. The trained model is going to verify by giving several testable data set and takes two datasets for training and testing. Trained model is tested rigorously to find out the accuracy of a model. This methodology gives efficient results for counting individual in real world.

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A fast background subtraction method using kernel density estimation for people counting

Cited by 4 publications

References 10 publications

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

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

Research on Foreground Object Recognition Tracking and Background Restoration in AIoT Era

Counting Individuals in an Image using Machine Learning Technique

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