A Multi-agent System for Infrared and Color Video Fusion

Gascueña, José Manuel; Serrano-Cuerda, Juan; Castillo, José Carlos; Fernández-Caballero, Antonio; López, María Teresa

doi:10.1007/978-3-319-07476-4_16

Cited by 4 publications

(3 citation statements)

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“…More concretely, the employed datasets are Corridor, Highway, and wetSnow. The datasets were chosen due to the variable complexity in the motion detection tasks [ 29 , 30 , 31 ]. In addition, these three benchmarks were chosen to demonstrate that ALI can detect movement in a variety of relatively complex situations.…”

Section: Resultsmentioning

confidence: 99%

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

López

Bermúdez

Montero

et al. 2018

Sensors

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Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.

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Section: Resultsmentioning

confidence: 99%

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

López

Bermúdez

Montero

et al. 2018

Sensors

Self Cite

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“…In another research [ 42 ], they proposed the multi-agent system for infrared and color video fusion. They showed the method of adaptively selecting the detection result from the thermal or visible light images based on the confidence level of the thermal or visible light image like the researches [ 39 , 40 ].…”

Section: Resultsmentioning

confidence: 99%

“…For the last difference between our method and these previous researches, in addition to the accuracies of human detection, we provide the processing speed of our method as shown in Table 7 considering the real-time application of intelligent surveillance system. However, previous researches did not show the results of processing speed [ 39 , 40 , 41 , 42 , 43 ].…”

Section: Resultsmentioning

confidence: 99%

Robust Pedestrian Detection by Combining Visible and Thermal Infrared Cameras

Lee

Choi

Jeon

et al. 2015

Sensors

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With the development of intelligent surveillance systems, the need for accurate detection of pedestrians by cameras has increased. However, most of the previous studies use a single camera system, either a visible light or thermal camera, and their performances are affected by various factors such as shadow, illumination change, occlusion, and higher background temperatures. To overcome these problems, we propose a new method of detecting pedestrians using a dual camera system that combines visible light and thermal cameras, which are robust in various outdoor environments such as mornings, afternoons, night and rainy days. Our research is novel, compared to previous works, in the following four ways: First, we implement the dual camera system where the axes of visible light and thermal cameras are parallel in the horizontal direction. We obtain a geometric transform matrix that represents the relationship between these two camera axes. Second, two background images for visible light and thermal cameras are adaptively updated based on the pixel difference between an input thermal and pre-stored thermal background images. Third, by background subtraction of thermal image considering the temperature characteristics of background and size filtering with morphological operation, the candidates from whole image (CWI) in the thermal image is obtained. The positions of CWI (obtained by background subtraction and the procedures of shadow removal, morphological operation, size filtering, and filtering of the ratio of height to width) in the visible light image are projected on those in the thermal image by using the geometric transform matrix, and the searching regions for pedestrians are defined in the thermal image. Fourth, within these searching regions, the candidates from the searching image region (CSI) of pedestrians in the thermal image are detected. The final areas of pedestrians are located by combining the detected positions of the CWI and CSI of the thermal image based on OR operation. Experimental results showed that the average precision and recall of detecting pedestrians are 98.13% and 88.98%, respectively.

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