A robust and enhanced approach for human detection in crowd

Khatoon, Robina; Saqlain, Syed Muhammad; Bibi, Shafina

doi:10.1109/inmic.2012.6511457

Cited by 12 publications

(8 citation statements)

References 10 publications

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“…Counting by detection: This kind of method allows to count people by a detector designed to detect each individual, for example, pedestrian detector [8], face detector and head-shoulder detector [9,10]. In pedestrian detection approach, a binary classifier is trained using common features, such as Haar wavelets and histograms of oriented gradients (HOG) [11].…”

Section: Related Workmentioning

confidence: 99%

Crowd counting considering network flow constraints in videos

Gao

Wang

et al. 2018

IET Image Processing

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The growth of the number of people in the monitoring scene may increase the probability of security threat, which makes crowd counting more and more important. Most of the existing approaches estimate the number of pedestrians within one frame, which results in inconsistent predictions in terms of time. This paper, for the first time, introduces a quadratic programming model with the network flow constraints to improve the accuracy of crowd counting. Firstly, the foreground of each frame is segmented into groups, each of which contains several pedestrians. Then, a regression-based map is developed in accordance with the relationship between low-level features of each group and the number of people in it. Secondly, a directed graph is constructed to simulate constraints on people's flow, whose vertices represent groups of each frame and arcs represent people moving from one group to another. Then, the people flow can be viewed as an integer flow in the constructed digraph. Finally, by solving a quadratic programming problem with network flow constraints in the directed graph, we obtain consistency in people counting. The experimental results show that the proposed method can reduce the crowd counting errors and improve the accuracy. Moreover, this method can also be applied to any ultramodern group-based regression counting approach to get improvements.

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Section: Related Workmentioning

confidence: 99%

Crowd counting considering network flow constraints in videos

Gao

Wang

et al. 2018

IET Image Processing

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“…With the advance and success in human detection, counting people becomes a by-product once each individual is correctly detected. The features that may be used include body, head, shoulder, skin, and hair [9,10]. The benefit of these approaches is that they have a high level of accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Some of detection-based approaches by using human shapes attempt to segment or detect every single person and then count them [11]. Nevertheless, some other detection-based approaches try to detect each independent motion in the image via clustering interest points on people tracked spanning time and then count the people [10,[12][13][14][15][16].…”

Section: Related Workmentioning

confidence: 99%

Counting People Based on Linear, Weighted, and Local Random Forests

Farhood

Jia

et al. 2017

2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)

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Recently, many works have been published for counting people. However, when being applied to real-world train station videos, they have exposed many limitations due to problems such as low resolution, heavy occlusion, various density levels and perspective distortions. In this paper, following the recent trend of regression-based density estimation, we present a linear regression approach based on local Random Forests for counting either standing or moving people on station platforms. By dividing each frame into sub-windows and extracting features with ground truth densities as well as learned weights, we perform a linear transformation for counting people to overcome the perspective problems of the existing patch-based approaches. We present improvements against several recent baselines on the UCSD dataset and a dataset of CCTV videos taken from a train station. We also show improvements in speed compared with the state-of-the-art models based on detection and Deep Learning.

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“…They employed expectation maximization for achieving their objective and provided comparison with fuzzy c-mean based segmentation. Khatoon et al (2012) used GMM for object segmentation where the primary task was human counting in crowd scenes.…”

Section: Segmentation Based On Mixture Of Gaussiansmentioning

confidence: 99%

A comparative study on mixture of Gaussians for object segmentation

Khatoon

2017

Int. j. adv. appl. sci

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Segmentation is the fundamental step in most of digital image processing and computer vision based applications for feature extraction. The purpose of segmentation is to partition an image into foreground and background. Numerous segmentation algorithms have been proposed for the last four decades ranging from degraded images; high and low contrast images, indoor video, outdoor videos, videos with static background and dynamic backgrounds. This paper presents evaluation and comparison of segmentation techniques used for real-time moving objects through static and adaptive number of Gaussians. The techniques are tested for both indoor and outdoor scenes. The comparison is presented on the basis of qualitative results and computational complexities.

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A robust and enhanced approach for human detection in crowd

Cited by 12 publications

References 10 publications

Crowd counting considering network flow constraints in videos

Crowd counting considering network flow constraints in videos

Counting People Based on Linear, Weighted, and Local Random Forests

A comparative study on mixture of Gaussians for object segmentation

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