A range-gated imaging flash Lidar based on the adjacent frame difference method

This paper makes study on the adjacent frame difference and algorithm realization of SOM i8dentification after improvement, of which it includes motion detection, target identification; the realized video surveillance module makes up the intelligent video surveillance that can reconstruct platform. Motion detection module adopts algorithm of adjacent frame difference after improvement, which can correctly mark the motion object. Target identification module adopts self-mapping nerve net after improvement, it is easier for hardware realization, and meanwhile the accuracy rate of identification is equal to classical algorithm.

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Object Tracking Algorithm Based on Camshift Combining Background Subtraction with Three Frame Difference

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.373-375.1116 ◽

2013 ◽

Vol 373-375 ◽

pp. 1116-1119 ◽

Cited By ~ 1

Author(s):

Quan Tang ◽

Shu Guang Dai ◽

Jie Yang

Keyword(s):

Probability Distribution ◽

Object Tracking ◽

Difference Method ◽

Background Subtraction ◽

Tracking Algorithm ◽

Target Area ◽

Characteristic Parameters ◽

Subtraction Method ◽

Frame Difference ◽

Camshift Algorithm

Camshift tracking algorithm is based on probability distribution of color , it is susceptible to be interfered by the same color in the background, which will lead to the failure of the target tracking. To overcome this problem it presented an improved Camshift tracking algorithm. It combined background subtraction method with three frame difference method to detect target, got rectangular characteristic parameters of the motion target area as the Camshift initialization parameters, replaced the general Camshift algorithm which is based on color feature. Experimental results show that Camshift algorithm combining the background subtraction method with three frame difference method can meet the requirements of the real-time and stability to a certain extent.

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Research on Moving Object Detection Algorithm Based on Improved Three Frame Difference Method and Optical Flow

2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) ◽

10.1109/imccc.2015.420 ◽

2015 ◽

Cited By ~ 14

Author(s):

Xiaowei Han ◽

Yuan Gao ◽

Zheng Lu ◽

Zhimin Zhang ◽

Dun Niu

Keyword(s):

Object Detection ◽

Optical Flow ◽

Difference Method ◽

Detection Algorithm ◽

Moving Object Detection ◽

Moving Object ◽

Frame Difference

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Frame Difference Method to Detect Fire and Compare the Accuracy and Precision with Vibe Method

Alinteri Journal of Agricultural Sciences ◽

10.47059/alinteri/v36i1/ajas21089 ◽

2021 ◽

Vol 36 (1) ◽

pp. 629-634

Author(s):

D. Niharika ◽

J. Mohana

Keyword(s):

Conventional Method ◽

Video Processing ◽

Difference Method ◽

High Accuracy ◽

Monitoring Systems ◽

Control Group ◽

Frame Difference ◽

Accuracy And Precision ◽

Improved Accuracy ◽

Experimental Group

Aim: In this paper, the main aim is to detect fire using a novel frame difference method and compare it with conventional method. This is based on video processing and computational methods to reduce the computational complexity. Materials and method: The method was performed over a sample size of 20. Same samples were applied for both the control group and experimental group. Improved accuracy detection was obtained using the proposed method. Results: The Accuracy and precision was found (94.03, 64.62) and (86.24,57.19) was obtained for the frame difference method and conventional method. It also shows a significance of 0.048 for accuracy and 0.018 for precision which is less than 0.05. Conclusion: It would be concluded that the frame difference method is producing high accuracy and precision when compared with the Vibe method. It is applicable for monitoring systems and home security.

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