Real-time person counting by propagating networks flows

Pätzold, Michael; Sikora, Thomas

doi:10.1109/avss.2011.6027296

Cited by 8 publications

(3 citation statements)

References 14 publications

(18 reference statements)

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“…Tracking methods based on these techniques are manifold and include e.g. graph-based approaches ( [11], [15]), particle filtering frameworks ( [3]) and methods using Random Finite Sets ( [6]). …”

Section: Introductionmentioning

confidence: 99%

Enhancing human detection using crowd density measures and an adaptive correction filter

Eiselein

Fradi

Keller

et al. 2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

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In this paper we present a method of improving a human detector by means of crowd density information. Human detection is especially challenging in crowded scenes which makes it important to introduce additional knowledge into the detection process. We compute crowd density maps in order to estimate the spatial distribution of people in the scene and show how it is possible to enhance the detection results of a state-of-the-art human detector by this information. The proposed method applies a self-adaptive, dynamic parametrization and as an additional contribution uses scene-adaptive learning of the human aspect ratio in order to reduce false positive detections in crowded areas. We evaluate our method on videos from different datasets and demonstrate how our system achieves better results than the baseline algorithm.

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Section: Introductionmentioning

confidence: 99%

Enhancing human detection using crowd density measures and an adaptive correction filter

Eiselein

Fradi

Keller

et al. 2013

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance

Self Cite

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“…[3][4][5][6] have focused on the common crisis of tracking, without particularly addressing the challenges of a multiple objects. Traditional tracking methods classically assume a static background or easily discernible moving objects, and, as a result, are limited to scenes with relatively few constituents 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Hybrid tracking model for multiple object videos using second derivative based visibility model and tangential weighted spatial tracking model

Philip

Mukesh

2016

IJCIS

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In the area of video surveillance, tracking model for multiple object video is still a challenging task since the objects are usually affected with inter-object occlusion, object confusion, different posing, environment with heavy clutter, small size of objects, similar appearance among objects, and interaction among the multiple objects In order to alleviate these challenges, literature presents different tracking models using spatial and visual information. Accordingly, in this paper, we have developed a hybrid tracking model for tracking the multiple objects from the videos using twofold architecture. At first, visibility model for tracking is proposed based on the second derivative model, which considers the second derivative function to predict the objects. Secondly, a spatial tracking model is proposed using tangential weighted function. Finally, these two contributions are effectively included in the hybrid tracking model for multiple object tracking and the performance analysis is carried out using two videos from UCSD dataset. From the results, we proved that the proposed hybrid tracking model achieves the Multiple Object Tracking Precision (MOTP) of 99% than the other exiting tracking models.

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“…Even though many research topics are available in video surveillance, tracking and behavior analysis from the crowded video are challenging problem because it has a number of applications including eventmonitoring, behavior modeling, traffic control and security applications [1][2][3][4]. In dense crowds, tracking and behavior detection is a major problem [5,6] because the large number of object is in close which makes it difficult to establish correspondences across frames.…”

Section: Introductionmentioning

confidence: 99%

Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

Kumar¹,

Bhatnagar²

2017

IJCIS

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In the current era, crowd behavior analysis is important topic due to the significance of video surveillance in the public area. Literature presents a handful of works for crowd behavior detection and analysis. Even though, the complicated challenges such as, low quality video, wide variation in the density of crowds and difficult motion patterns pose a complicated challenges for the researchers in crowd behavior detection. In order to alleviate these issues, we develop a crowd behavior detection system using hybrid tracking model and integrated features enabled neural network. The proposed crowd behavior detection system estimate the direction of movement of objects as well their activity using proposed GLM-based neural network. The proposed GLM-based neural network integrates the LM algorithm with genetic algorithm to improve the learning process of neural network. The performance of the proposed crowd behavior detection algorithm is validated with five different video and the performance is extensively analyzed using accuracy. From research outcome, we proved that the proposed system obtained the maximum accuracy of 95% which is higher than the existing methods taken for comparison.

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Real-time person counting by propagating networks flows

Cited by 8 publications

References 14 publications

Enhancing human detection using crowd density measures and an adaptive correction filter

Enhancing human detection using crowd density measures and an adaptive correction filter

Hybrid tracking model for multiple object videos using second derivative based visibility model and tangential weighted spatial tracking model

Crowd Behavior Recognition Using Hybrid Tracking Model and Genetic algorithm Enabled Neural Network

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