Automatic detection of dangerous motion behavior in human crowds

Krausz, Barbara; Bauckhage, Christian

doi:10.1109/avss.2011.6027326

Cited by 23 publications

(22 citation statements)

References 21 publications

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“…We used the one-pass evaluation criteria proposed in [27], with all 50 image sequence from their data set and four additional. In case of the additional four, Caviar is from CAVIAR 2 data set, CupOcc and Cheetah are from [29], and GreenMan is from a part of the festival dataset in [17]. These sequences contain various situations including occlusions, background clutters, outer-plane motions, non-rigid deformation, illumination changes, and rapid camera movements.…”

Section: Experimental Settingsmentioning

confidence: 99%

Visual tracking in complex scenes through pixel-wise tri-modeling

Jeong

Lee

et al. 2015

Machine Vision and Applications

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In this paper we propose a pixel-wise visual tracking method using a novel tri-model representation. The newly proposed tri-model is composed of three models, which each model learns the target object, the background, and other non-target moving objects online. The proposed method performs tracking by simultaneous estimation of the holistic position of the target object and the pixel-wise labels. By utilizing the information in the background and the foreground models as well as the target model, our method obtains robust results even under background clutters and partial occlusions in complex scenes. Furthermore, our method is able to give pixel-wise results, and uses them in the learning process to prevent drifting. The method is extensively tested against seven representative trackers both quantitatively and qualitatively showing promising results.

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Section: Experimental Settingsmentioning

confidence: 99%

Visual tracking in complex scenes through pixel-wise tri-modeling

Jeong

Lee

et al. 2015

Machine Vision and Applications

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“…Finally use dynamic Bayesian network (DBN) to learn each of the unique paths detected in the scene so that normal behavior can be distinguished from an abnormal one. Reference [5] develops a system for the detection and early warning of dangerous situations during mass events. This method computes dense optical ow elds and Based on histograms of optical ow, proposes methods to automatically detect dangerous motion behavior in crowds exist.…”

Section: Introductionmentioning

confidence: 99%

Using optical flow and spectral clustering for behavior recognition and detection of anomalous behaviors

Feizi

Aghagolzadeh

Seyedarabi

2013

2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP)

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In this paper we propose an efficient method for behavior recognition and identification of anomalous behavior in video surveillance data. This approach consists of two phases of training and testing. In the training phase, first, we use background subtraction method to extract the moving pixels. Then optical flow vectors are extracted for moving pixels. We propose behavior features of each pixel as the average all optical flow vectors in the pixel over several frames in video data. Next, we use spectral clustering to classify behaviors wherein pixels that have similar behavior features are clustered together. Then we obtain a behavior model for each cluster using the normal distribution of the samples. Once the behavior models are obtained, in the testing phase, we use these models to detect anomalous behavior in a test video of the same scene. Experimental results on video surveillance sequences show the effectiveness and speed of proposed method.

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“…Moreover, Krausz and Bauckhage in [2] proposed a system works automatically for identifying the critical situation during an increased number of the public by using system alarms, but this system showed error in the detection even at normal state. Dee and Caplier in [6] suggest a system depend on representing the movement pattern of the crowd.…”

Section: Related Workmentioning

confidence: 99%

Crowd Counting Mapping to make a Decision

Faisal¹,

Sleit²,

Al-Sayyed³

2018

ijacsa

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Abstract-Congestion typically occurs when the number of crowds exceeds the capacity of facilities. In some cases, when buildings have to be evacuated, people might be trapped in congestion and cannot escape from the building early enough which might even lead to stampedes. Crowd Congestion Mapping (CCM) is a system that enables organizations to find information about the crowd congestion in target places. This project provides the ability to make the right decision to determine the reasons that led to that and to do the appropriate procedures to avoid this from happening again by optimizing locations and dimensions of the emergency exits less congested path on the target places. The system collects crowd congestion data from the locations and makes it available to corporations via target map. The congestion is plotted on target place map, for example, the red line for highly congested location, the pink line for mildly congested location and green line for free flow of humans in the location.

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Automatic detection of dangerous motion behavior in human crowds

Cited by 23 publications

References 21 publications

Visual tracking in complex scenes through pixel-wise tri-modeling

Visual tracking in complex scenes through pixel-wise tri-modeling

Using optical flow and spectral clustering for behavior recognition and detection of anomalous behaviors

Crowd Counting Mapping to make a Decision

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