Real-Time People Counting from Depth Images

Nalepa, Jakub; Szymanek, Janusz; Kawulok, Michał

doi:10.1007/978-3-319-18422-7_34

Cited by 13 publications

(3 citation statements)

References 24 publications

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“…12. First Row shows the example of depth images, (d) [30], third row contains images of RGB color space while last row include YCbCr [12]and HSV [21] color model, respectively.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

Person Detection from Overhead View: A Survey

Ahmad¹,

Ahmed²,

Ullah³

et al. 2019

IJACSA

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In recent years, overhead view based person detection gained importance, due to handling occlusion problem and providing better coverage in scene, as compared to frontal view. In computer vision, overhead based person detection holds significant importance in many applications including person detection, person counting, person tracking, behavior analysis and occlusion free surveillance system, etc. This paper aims to provide a comprehensive survey on recent development and challenges related to person detection from top view. To the best of our knowledge, it is the first attempt which provides the survey of different overhead person detection techniques. This paper provides an overview of state of the art overhead based person detection methods and guidelines to choose the appropriate method in real life applications. The techniques are divided into two main categories: the blob-based techniques and the feature-based techniques. Various detection factors such as field of view, region of interest, color space, image resolution are also examined along with a variety of top view datasets.

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“…12. First Row shows the example of depth images, (d) [30], third row contains images of RGB color space while last row include YCbCr [12]and HSV [21] color model, respectively.…”

Section: A Blob Based Techniquesmentioning

confidence: 99%

Person Detection from Overhead View: A Survey

Ahmad¹,

Ahmed²,

Ullah³

et al. 2019

IJACSA

View full text Add to dashboard Cite

show abstract

“…In both cases, a background segmentation technique is often exploited to facilitate the extraction of features and ease the detection process. People detection approaches based on unsupervised techniques have shown good efficiency in real world applications [1,5,8,13,16,18], but their performance is compromised when people appear in static postures. In addition, the performance is heaviliy subject to the quality of background subtraction.…”

Section: Related Workmentioning

confidence: 99%

WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Villamizar

Martínez-González

Canévet

et al. 2018

2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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We propose a deep-learning approach for people detection on depth imagery. The approach is designed to be deployed as an autonomous appliance for identifying people attacks and intrusion in video surveillance scenarios. To this end, we propose a fully-convolutional and sequential network, named WatchNet, that localizes people in depth images by predicting human body landmarks such as head and shoulders. We use a large synthetic dataset to train the network with abundant data and generate automatic annotations. Adaptation to real data is performed via fine tuning with real depth images. The proposed method is validated in a novel and challenging database with about 29k top view images collected from several sequences including different people assaults. A comparative evaluation is given between our approach and other standard methods, showing remarkable detection results and efficiency. The network runs in 10 and 28 FPS using CPU and GPU, respectively.

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“…Most of the previous works can only count moving people from a single camera, and they fail to count still people or situations when occlusions are very frequent and when there is a crowd. Possible applications can be: safety and security in crowded environments, people flow analysis and access control, as well as counting [ 56 , 57 , 58 ]. Actual tracking accuracy of top-view cameras overperforms all other tracking methods in crowded environments, with accuracies up to 99%.…”

Section: Introductionmentioning

confidence: 99%

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

Paolanti

Romeo

Liciotti

et al. 2018

Sensors

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Person re-identification is an important topic in retail, scene monitoring, human-computer interaction, people counting, ambient assisted living and many other application fields. A dataset for person re-identification TVPR (Top View Person Re-Identification) based on a number of significant features derived from both depth and color images has been previously built. This dataset uses an RGB-D camera in a top-view configuration to extract anthropometric features for the recognition of people in view of the camera, reducing the problem of occlusions while being privacy preserving. In this paper, we introduce a machine learning method for person re-identification using the TVPR dataset. In particular, we propose the combination of multiple k-nearest neighbor classifiers based on different distance functions and feature subsets derived from depth and color images. Moreover, the neighborhood component feature selection is used to learn the depth features’ weighting vector by minimizing the leave-one-out regularized training error. The classification process is performed by selecting the first passage under the camera for training and using the others as the testing set. Experimental results show that the proposed methodology outperforms standard supervised classifiers widely used for the re-identification task. This improvement encourages the application of this approach in the retail context in order to improve retail analytics, customer service and shopping space management.

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Real-Time People Counting from Depth Images

Cited by 13 publications

References 24 publications

Person Detection from Overhead View: A Survey

Person Detection from Overhead View: A Survey

WatchNet: Efficient and Depth-based Network for People Detection in Video Surveillance Systems

Person Re-Identification with RGB-D Camera in Top-View Configuration through Multiple Nearest Neighbor Classifiers and Neighborhood Component Features Selection

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