Real-Time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)

Siva, Parthipan; Shafiee, Mohammad Javad; Jamieson, Michael; Wong, Alexander

doi:10.1109/cvprw.2016.115

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…Crowd counting performance is evaluated by three universal quantitative metrics: mean absolute error (MAE), mean square error (MSE) and mean deviation error (MDE)

MAE = \frac{1}{N} {false\sum}_{1}^{N} (||, y_{i} - {\hat{y}}_{i}^{})

MSE = \frac{1}{N} {false\sum}_{1}^{N} {false(y_{i} - {\overset{false^}{y}}_{i} false)}^{2}

MDE = \frac{1}{N} {false\sum}_{1}^{N} \frac{(||, y_{i} - {\overset{false^}{y}}_{i})}{y_{i}}

We choose several relative algorithms to compare against: recent pedestrian detector (Detector [29]), histogram of moving gradients [30], least square support vector regression (LSSVR [31]), kernel ridge regression (KRR [32]), random forest regression (RFR [33]), Gaussian process regression (GPR [24]), ridge regression (RR [34]) and cumulative attribute ridge regression (CA‐RR [10]). In particular, the HoMG [30] is regarded as a very appropriate comparison by us. Only because same as our method, HoMG is also a local method using the overlapped sliding window and extracting Hog features from differenced gradients.…”

Section: Methodsmentioning

confidence: 99%

Scene invariant crowd counting using multi‐scales head detection in video surveillance

2018

IET Image Processing

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“…Crowd counting performance is evaluated by three universal quantitative metrics: mean absolute error (MAE), mean square error (MSE) and mean deviation error (MDE)

MAE = \frac{1}{N} {false\sum}_{1}^{N} (||, y_{i} - {\hat{y}}_{i}^{})

MSE = \frac{1}{N} {false\sum}_{1}^{N} {false(y_{i} - {\overset{false^}{y}}_{i} false)}^{2}

MDE = \frac{1}{N} {false\sum}_{1}^{N} \frac{(||, y_{i} - {\overset{false^}{y}}_{i})}{y_{i}}

Section: Methodsmentioning

confidence: 99%

Scene invariant crowd counting using multi‐scales head detection in video surveillance

2018

IET Image Processing

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“…This work uses the common standard evaluation metrics to test the methods: mean absolute error (MAE) and the root mean squared error (RMSE) [45]. They are defined as follows, Equations (9) and (10).…”

Section: Evaluation Metricsmentioning

confidence: 99%

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Zhao

Min

Wei

et al. 2021

IET Image Processing

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Crowd counting aims to count the number of people in crowded scenes, which is important to the security systems, traffic control and so on. The existing methods typically using local features cannot properly handle the perspective distortion and the varying scales in congested scene images, and henceforth perform wrong people counting. To alleviate this issue, this study proposes a multi-scale residual feature-aware network (MSR-FAN) that combines multi-scale features using multiple receptive field sizes and learns the featureaware information on each image. The MSR-FAN is trained end-to-end to generate highquality density map and evaluate the crowd number. The method consists of three parts. To handle the perspective changes problem, the first part, the direction-based featureenhanced network, is designed to encode the perspective information in four directions based on the initial image feature. The second part, the proposed multi-scale residual block module, gets the global information to handle the represent the regional feature better. This module explores features of different scales as well as reinforce the global feature. The third part, the feature-aware block, is designed to extract the feature hidden in the different channels. Experiment results based on benchmark datasets show that the proposed approach outperforms the existing state-of-the-art methods.

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“…The researches [19][20][21][22][23][24][25] about crowd counting are too rich to elaborate all of them. Next, we briefly review some of them.…”

Section: Related Workmentioning

confidence: 99%

Crowd counting with segmentation attention convolutional neural network

Chen

Wang

2021

IET Image Processing

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Deep learning occupies an undisputed dominance in crowd counting. This paper proposes a novel convolutional neural network architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposed SegCrowdNet still adaptively highlights the human head region and suppresses the non‐head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi‐scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of this proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that the proposed SegCrowdNet achieves excellent performance compared with the state‐of‐the‐art methods.

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Real-Time, Embedded Scene Invariant Crowd Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)

Cited by 17 publications

References 22 publications

Scene invariant crowd counting using multi‐scales head detection in video surveillance

Scene invariant crowd counting using multi‐scales head detection in video surveillance

MSR‐FAN: Multi‐scale residual feature‐aware network for crowd counting

Crowd counting with segmentation attention convolutional neural network

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