scholarly journals Computer Vision Based Smoke Detection Method by Using Colour and Object Tracking

2016 ◽  
Author(s):  
Ung Hwang ◽  
Jechang Jeong
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Detection Method ◽  
Smoke Detection

Improved SURF Detection Combined with Dual FLANN Matching and Clustering Analysis

2014 ◽  
Vol 556-562 ◽  
pp. 2792-2796 ◽  
Author(s):  
Jun Fei Li ◽  
Geng Wang ◽  
Qiang Li
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Real Time ◽  
Clustering Analysis ◽  
Detection Method ◽  
Haar Wavelet ◽  
Time Cost ◽  
Speed Up ◽  
Real Time Applications ◽  
Speed Up Robust Feature

In this paper, an improved object detection method based on SURF (Speed-Up Robust Feature) is presented. SURF is a widely used method in computer vision. But it’s still not efficient enough to apply in real-time applications, such as real time object tracking. To reduce the time cost, the traditional descriptor of SURF is altered. Triangle and diagonal descriptor is adopted to replace the Haar wavelet calculation. Then dual matching approach based on FLANN is employed. Thus matching errors can be cut down. Besides, the traditional SURF does not give the accurate region of the target. To restrict the area, clustering analysis is used which is promoted from K-WMeans. Experimental work demonstrates the proposed approach achieve better effect than traditional SURF in real scenarios.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Research on Fire Smoke Detection Method Based on Video Image

2021 ◽  
Author(s):  
Xiaobo Xu ◽  
Guoxuan Tang ◽  
Jiayi Wu ◽  
Changzhou Geng
Keyword(s):  
Detection Method ◽  
Video Image ◽  
Smoke Detection ◽  
Fire Smoke

scholarly journals New Edge Detection Method for Indonesian Batik

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Thomas Adi Purnomo Shidi ◽  
Suyoto Suyoto
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Detection Method ◽  
New Method ◽  
Analysis Method ◽  
Unique Pattern ◽  
Detection Analysis ◽  
Edge Detection Method

Abstrak. Metode Baru Deteksi Tepi untuk Batik Indonesia. Didalam paper ini, diusulkan sebuah metode pendeteksi baru untuk motif batik. Deteksi tepi sudah sangat sering digunakan didalam pemrosesan gambar. Batik motif adalah salah satu contoh gambar yang memiliki bentuk yang unik dan menarik untuk dianalisis. Metode yang digunakan pada paper ini adalam metode canny dan prewit dan akan menghasilkan metode baru yaitu metode Thomas. Perbedaan antara metode dan hasil akan dilihat dari sisi ketepatan, qualitas hasil dan kejelasan. Contoh batik yang akan digunakan adalah motif parang, motife lereng dan udan liris. Ketiga batik tersebut memiliki pola  yang unik. Kata kunci : Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris. Abstract. New Edge Detection Method for Indonesian Batik. In this paper, we propose a new edge detection analysis method on batiks motif. Edge detection has been oftenly  used in computer vision and image processing. Indonesian  Batiks motif are some example of graphic picture that has unique pattern that interesting to analyse. The method that used for example on this paper are canny and prewit and produce a new method, thomas method. the different  amongs the method, the result of comparison appears on quality, accuracy and clarity. The example that we use are parang batiks motive, lereng batiks motive, and udan liris batiks motive. Three of batiks motive above are have unique pattern. Keywords: Canny, Prewitt, Thomas, Batik, Parang, Lereng, Udan liris.

scholarly journals Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping

2019 ◽  
Vol 9 (18) ◽  
pp. 3729 ◽  
Author(s):  
Bao ◽  
Tan ◽  
Liu ◽  
Miao
Keyword(s):  
Computer Vision ◽  
Visual Detection ◽  
Detection Method ◽  
Transformation Matrix ◽  
Normal Vector ◽  
Vision Measurement ◽  
Extrinsic Parameters ◽  
Perspective Effect ◽  
Rotation Transformation ◽  
Inverse Perspective Mapping

A computer vision method for measuring multiple pointer meters is proposed based on the inverse perspective mapping. First, the measured meter scales are used as the calibration objects to obtain the extrinsic parameters of the meter plane. Second, normal vector of the meter plane can be acquired by the extrinsic parameters, obtaining the rotation transformation matrix of the meter image. Then, the acquired meter image is transformed to a position both parallel to the meter plane and near the main point by the rotation transformation matrix and the extrinsic parameters, eliminating the perspective effect of the acquired image. Finally, the transformed image is tested by the visual detection method to obtain the readings of the pointer meter, improving measurement precision. The results of the measurement verify the effectiveness and accuracy of the method.

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