An Intelligent Cellular Automaton Model for Crowd Evacuation in Fire Spreading Conditions

Zhou, Yan; Jorgensen, Zach; Inge, Meador

doi:10.1109/ictai.2007.67

Cited by 23 publications

(3 citation statements)

References 13 publications

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“…Prior and more recent works proved that CA are very effective in simulating environmental systems, because they can capture the essential features of systems where global behaviour arises from the collective effect of simple components which interact locally [24], [25]. Nontrivial CA are obtained whenever the dependence on the values at each cell is nonlinear [26]. As a result, any physical system satisfying differential equations may be approximated by a CA, by introducing finite differences and discrete variables [27], [28].…”

Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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Illegal migration as well as wildfires constitute commonplace situations in southern European countries, where the mountainous terrain and thick forests make the surveillance and location of these incidents a tall task. This territory could benefit from Unmanned Aerial Vehicles (UAVs) equipped with optical and thermal sensors in conjunction with sophisticated image processing and computer vision algorithms, in order to detect suspicious activity or prevent the spreading of a fire. Taking into account that the flight height is about to two kilometers, human and fire detection algorithms are mainly based on blob detection. For both processes thermal imaging is used in order to improve the accuracy of the algorithms, while in the case of human recognition information like movement patterns as well as shadow size and shape are also considered. For fire detection a blob detector is utilized in conjunction with a color based descriptor, applied to thermal and optical images, respectively. Unlike fire, human detection is a more demanding process resulting in a more sophisticated and complex algorithm. The main difficulty of human detection originates from the high flight altitude. In images taken from high altitude where the ground sample distance is not small enough, people appear as small blobs occupying few pixels, leading corresponding research works to be based on blob detectors to detect humans. Their shadows as well as motion detection and object tracking can then be used to determine whether these regions of interest do depict humans. This work follows this motif as well; nevertheless, its main novelty lies in the fact that the human detection process is adapted for high altitude and vertical shooting images in contrast with the majority of other similar works where lower altitudes and different shooting angles are considered. Additionally, in the interest of making our algorithms as fast as possible in order for them to be used in real time during the UAV flights, parallel image processing with the help of a specialized hardware device based on Field Programmable Gate Array (FPGA) is being worked on.

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Section: Hardwarementioning

confidence: 99%

Human and Fire Detection from High Altitude UAV Images

Giitsidis

Karakasis

Γαστεράτος

et al. 2015

2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing

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“…Based on the above studies, in this paper, the cellular automaton model, which has been successfully applied in many areas [4,5], such as forest fire propagation [6], evacuation of people in a hall [7] and so on, is used to simulate a carriage evacuation. A special program CARUN is developed to deal with the evacuation of the passengers in train in the emergency.…”

Section: Introductionmentioning

confidence: 99%

Simulation of evacuation processes using cellular automaton model for carriage passenger dynamic

Chen

Sun

2009

2009 IEEE International Conference on Intelligent Computing and Intelligent Systems

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A cellular automata model (CAM) is presented to simulate the passenger evacuation of carriage in an emergency. Some special algorithm is introduced considering simple human judgment to make the rules more reasonable. Based on the above model, a program CARUN is developed. Considering the special geometry and boundary conditions, the simulation of the evacuation process for the passenger in carriage is performed. And effects of some important factors were emphatically discussed such as judgment criterion for biased exit, the number of the persons, and person distribution positions. Evacuation times for different scenarios were obtained and analyzed comparatively. The results indicate that the program CARUN can simulate the evacuation in carriage appropriately and some practice phenomena related to the different human behaviors could be observed.

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“…Cellular automata are used in [12] to simulate crowd evacuation in response to a fire. Two simultaneous processes are modeled: fire spreading and crowd evacuation.…”

Section: Non-biological Cellularmentioning

confidence: 99%

Performance Enhancement and Equivalence Criteria for Cellular Automaton-Based Tumor Simulations

Farnham¹

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List of Figures 4.1 Tumor cell population before diffusion adjustments 4.2 Tumor cell population after diffusion adjustments 4.3 Starting tumor population 4.4 Healthy cell population without growth limiter 4.5 Zero-flux boundary conditions 4.6 Tumor cells: low nutrient concentrations at boundary 4.7 Tumor cells: high nutrient concentrations at boundary 5.1 Steady-state locations with different threshold settings 5.2 Steady-state locations at different timesteps 6.1 Example of the hierarchical data structure 6.2 Suppression of updates using the data structure 7.1 Cell population difference between modified and canonical simulations 7.2 Location of update suppression 7.3 Locations of cell population differences 7.4 Change in an equivalence region over time 7.5 Equivalence region of an outer tissue block 7.6 Non-equivalent tissue locations 7.7 Equivalence region created from reasonable result states 7.8 Example of translation with single element-based equivalence 7.9 Example of translation with block-based equivalence 8.1 Data structure with bitwise operations 8.2 Gene regulatory network model

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An Intelligent Cellular Automaton Model for Crowd Evacuation in Fire Spreading Conditions

Cited by 23 publications

References 13 publications

Human and Fire Detection from High Altitude UAV Images

Human and Fire Detection from High Altitude UAV Images

Simulation of evacuation processes using cellular automaton model for carriage passenger dynamic

Performance Enhancement and Equivalence Criteria for Cellular Automaton-Based Tumor Simulations

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