scholarly journals Colour Detection Based Robotic Cart Using Raspberry Pi

2021 ◽  
pp. 615-620
Author(s):  
Mr. M. Senthil Murugan ◽  
Renuka E. ◽  
Vinodhini M.
Keyword(s):  
Moving Objects ◽  
Detection Algorithm ◽  
Color Pattern ◽  
Raspberry Pi ◽  
Safe Distance ◽  
Color Detection ◽  
Left And Right ◽  
Computer Vision Applications ◽  
Color Scheme ◽  
Color Tracking

One of the most critical subjects of embedded vision is color tracking in real time. Many computer vision applications begin by detecting and tracking moving objects in video scenes. Customers arriving at hypermarkets may benefit from this concept. A color detection algorithm locates pixels in an image that fit a predetermined color scheme. To differentiate detected pixels from the rest of the image, the color of the detected pixels can be modified. The robot is programmed to track objects by turning left and right to keep the target in view and driving forward and backward to keep the distance between the robot and the object steady. By maintaining a surrounding distance, detection of other objects of the same color pattern is ignored. By keeping a safe distance between the user and the robot, other objects of the same color pattern are not detected. The camera on an ARM11 Raspberry Pi computer attached to the robot is used to capture images. Using inbuilt python files, the acquired image is processed to locate the color using RGB varying pattern methodology. To make the product work smarter, this system also includes automatic billing via RFID reader and tag. The new concept of image processing domain is based on this device theory.

scholarly journals A real-time video smoke detection algorithm based on Kalman filter and CNN

2021 ◽  
Author(s):  
Alessio Gagliardi ◽  
Francesco de Gioia ◽  
Sergio Saponara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Scale ◽  
Moving Objects ◽  
Time Window ◽  
Hybrid Approach ◽  
Detection Algorithm ◽  
Regions Of Interest ◽  
Raspberry Pi ◽  
Smoke Detection

AbstractSmoke detection represents a critical task for avoiding large scale fire disaster in industrial environment and cities. Including intelligent video-based techniques in existing camera infrastructure enables faster response time if compared to traditional analog smoke detectors. In this work presents a hybrid approach to assess the rapid and precise identification of smoke in a video sequence. The algorithm combines a traditional feature detector based on Kalman filtering and motion detection, and a lightweight shallow convolutional neural network. This technique allows the automatic selection of specific regions of interest within the image by the generation of bounding boxes for gray colored moving objects. In the final step the convolutional neural network verifies the actual presence of smoke in the proposed regions of interest. The algorithm provides also an alarm generator that can trigger an alarm signal if the smoke is persistent in a time window of 3 s. The proposed technique has been compared to the state of the art methods available in literature by using several videos of public and non-public dataset showing an improvement in the metrics. Finally, we developed a portable solution for embedded systems and evaluated its performance for the Raspberry Pi 3 and the Nvidia Jetson Nano.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

Moving Objects Detection Algorithm Based on Three-Dimensional Gaussian Mixture Codebook Model Using XYZ Color Model

2013 ◽  
Vol 347-350 ◽  
pp. 3505-3509 ◽  
Author(s):  
Jin Huang ◽  
Wei Dong Jin ◽  
Na Qin
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Moving Objects ◽  
Three Dimensional ◽  
Gaussian Mixture ◽  
Detection Algorithm ◽  
Color Model ◽  
Moving Objects Detection ◽  
Objects Detection ◽  
Model Algorithm

In order to reduce the difficulty of adjusting parameters for the codebook model and the computational complexity of probability distribution for the Gaussian mixture model in intelligent visual surveillance, a moving objects detection algorithm based on three-dimensional Gaussian mixture codebook model using XYZ color model is proposed. In this algorithm, a codebook model based on XYZ color model is built, and then the Gaussian model based on X, Y and Z components in codewords is established respectively. In this way, the characteristic of the three-dimensional Gaussian mixture model for the codebook model is obtained. The experimental results show that the proposed algorithm can attain higher real-time capability and its average frame rate is about 16.7 frames per second, while it is about 8.3 frames per second for the iGMM (improved Gaussian mixture model) algorithm, about 6.1 frames per second for the BM (Bayes model) algorithm, about 12.5 frames per second for the GCBM (Gaussian-based codebook model) algorithm, and about 8.5 frames per second for the CBM (codebook model) algorithm in the comparative experiments. Furthermore the proposed algorithm can obtain better detection quantity.

scholarly journals An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xin Wang ◽  
Xinzheng Niu ◽  
Jiahui Zhu ◽  
Zuoyan Liu
Keyword(s):  
Community Detection ◽  
Trajectory Analysis ◽  
Moving Objects ◽  
Clustering Algorithms ◽  
Similarity Measures ◽  
Detection Algorithm ◽  
Similarity Matrix ◽  
Trajectory Data ◽  
Trajectory Similarity ◽  
Community Detection Algorithm

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

scholarly journals Moving Objects Detection & Recognition using Hybrid Canny Edge Detection Algorithm in Digital Image Processing

2019 ◽  
Vol 8 (9S3) ◽  
pp. 56-60
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Detection Method ◽  
Learning Algorithm ◽  
Detection Algorithm ◽  
Moving Objects Detection ◽  
Canny Edge ◽  
Objects Detection ◽  
Canny Edge Detection Algorithm ◽  
Day By Day

Recognition and detection of an object in the watched scenes is a characteristic organic capacity. Animals and human being play out this easily in day by day life to move without crashes, to discover sustenance, dodge dangers, etc. Be that as it may, comparable PC techniques and calculations for scene examination are not all that direct, in spite of their exceptional advancement. Object detection is the process in which finding or recognizing cases of articles (for instance faces, mutts or structures) in computerized pictures or recordings. This is the fundamental task in computer. For detecting the instance of an object and to pictures having a place with an article classification object detection method usually used learning algorithm and extracted features. This paper proposed a method for moving object detection and vehicle detection.

scholarly journals A Real Time Image Processing Bird Repellent System Using Raspberry Pi

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Oluwole Arowolo ◽  
Adefemi A Adekunle ◽  
Joshua A Ade-Omowaye
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Moving Objects ◽  
Detection System ◽  
Local Binary Patterns ◽  
Raspberry Pi ◽  
Feature Identification ◽  
Data Set ◽  
Haar Features ◽  
Haar Feature

Rice is one of the most consumed foods in Nigeria, therefore it’s production should be on the high as to meet the demand for it. Unfortunately, the quantity of rice produced is being affected by pests such as birds on fields and sometimes in storage. Due to the activities of birds, an effective repellent system is required on rice fields. The proposed effective repellent system is made up of hardware components which are the raspberry pi for image processing, the servo motors for rotation of camera for better field of view controlled by Arduino connected to the raspberry pi, a speaker for generating predator sounds to scare birds away and software component consisting of python and Open Cv library for bird feature identification. The model was trained separately using haar features, HOG (Histogram of Oriented Gradients) and LBP (Local Binary Patterns).Haar features resulted in the highest accuracy of 76% while HOG and LBP were, 27% and 72% respectively. Haar trained model was tested with two recorded real time videos with birds, the false positives were fairly low, about 41%. This haar feature trained model can distinguish between birds and other moving objects unlike a motion detection system which detects all moving objects. This proposed system can be improved to have a higher accuracy with a larger data set of positive and negative images. Keywords—Electronic pest repeller Haar cascade classifier, ultrasonic

Smooth Track-Keeping and Real Time Obstacle Detection Algorithm and its PID Controller Implementation for an Automated Wheeled Line Following Robot

2011 ◽  
Vol 201-203 ◽  
pp. 1966-1971 ◽  
Author(s):  
Mohammad Rokonuzzaman ◽  
Shah Muhammad Ferdous ◽  
Enaiyat Ghani Ovy ◽  
Md. Ashraful Hoque
Keyword(s):  
Mobile Robot ◽  
Pid Controller ◽  
Material Handling ◽  
Moving Objects ◽  
Detection Algorithm ◽  
Acute Angle ◽  
Obstacle Detection ◽  
Response Position ◽  
Wavy Line ◽  
Line Following

Line following automated robots is extensively used in industries for smooth running of production. This paper presents a simple and effective solution for path tracking problem for a wheeled automated mobile robot which can be used for material handling in industries. A PID controller has been used for controlling the robot which is capable of moving safely by smooth track-keeping in partially structured environment without any collision with static or moving objects. The purpose of the project is to build a mobile robot which will provide fast, smooth, accurate and safe movement in any given line or track. A straight or wavy line would be simple to follow whereas aT-junction, 90 degree bends, acute angle bends and grid junctions would be difficult to navigate through. This is due to the physical kinematics constraints which are limited to motor response, position and turning radius of the robot. A line sensor configuration has been proposed to improve the navigation reliability of the mobile robot which uses differential drive system. A dynamic algorithm has been developed for detecting and following a specified line which ensures the reliable and safe movement of the robot.

Low-Cost Smart Heads-Up Display Assist System for Automobiles

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Gautham G ◽  
Deepika Venkatesh ◽  
A. Kalaiselvi
Keyword(s):  
Fuel Economy ◽  
Low Cost ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Raspberry Pi ◽  
Worst Case ◽  
The Road ◽  
Traffic Conditions ◽  
Metropolitan Cities ◽  
Ingenious Method

In recent years, due to the increasing density of traffic every year, it is been a hassle for drivers in metropolitan cities to maintain lane and speeds on road. The drivers usually waste time and effort in idling their cars to maintain in traffic conditions. The drivers get easily frustrated when they tried to maintain the path because of the havoc created. Transportation Institute found that the odds of a crash(or near crash) more than doubled when the driver took his or her eyes off the road formore than two seconds. This tends to cause about 23% of accidents when not following their lane paths. In worst case the fuel economy often drops and tends to cause increase in pollution about 28% to 36% per vehicle annually. This corresponds to the wastage of fuel. Owing to this problem, we proposed an ingenious method by which the lane detection can be made affordable and applicable to existing automobiles. The proposed prototype of lane detection is carried over with a temporary autonomous bot which is interfaced with Raspberry pi processor, loaded with the lane detection algorithm. This prototype bot is made to get live video which is then processed by the algorithm. Also, the preliminary setups are carried over in such a way that it is easily implemented and accessible at low cost with better efficiency, providing a better impact on future automobiles.

Improved Illumination Independent Moving Object Detection Algorithm

2014 ◽  
pp. 15-22
Keyword(s):  
Real World ◽  
Moving Objects ◽  
Image Sequence ◽  
Detection Algorithm ◽  
Moving Object Detection ◽  
Moving Object ◽  
Detection Technique ◽  
Real Image ◽  
Illumination Changes ◽  
Precision And Accuracy

A real time change detection technique is proposed in order to detect the moving objects in a real image sequence. The described method is independent of the illumination of the analyzed scene. It is based on a comparison of corresponding pixels that belong to different frames and combines time and space analysis, which augments the algorithm’s precision and accuracy. The efficiency of the described technique is illustrated on a real world interior video sequence recorded under significant illumination changes.

Image Identification of a Moving Object Based on an Improved Canny Edge Detection Algorithm

2018 ◽  
Author(s):  
Yang Liu ◽  
Lingyu Sun ◽  
Lijun Li ◽  
Yiben Zhang ◽  
Zongmiao Dai ◽  
...  
Keyword(s):  
Edge Detection ◽  
Moving Objects ◽  
Median Filter ◽  
Critical Role ◽  
Detection Algorithm ◽  
Object Identification ◽  
Moving Object ◽  
Detection Accuracy ◽  
Image Process ◽  
Canny Operator

Edge detection plays an increasingly critical role in image process community, especially for moving object identification problems. For this case, the target object can be captured straightly via the edges beside which there is an obvious jump of grey value or texture. Nowadays, Canny operator has gained great popularity as it shows higher anti-noise performance and presents better detection accuracy in comparison with other edge detection operators like Robert’s, Sobel’s, Prewitt’s etc. However, the Gaussian filter associated with the classic Canny operator is sometimes too simple to decrease the all-type-noise. Additionally, in order to enhance the detection accuracy and lower the pseudo-edges detection ratio, two thresholds, high and low, are chosen artificially which have actually limited the adaptability of the algorithm. In this work, a compound filter, Gaussian-Median filter, is proposed to improve the smoothing effect. The self-adaptive multi-threshold Otsu algorithm is realized to determine the high/low threshold automatically according to the grey value statistic. Image moment method is conducted on basis of the detected moving object edges to locate the centroid and to compute the principal orientation. The experimental results based upon locating the edges of both static and moving objects proved the good robustness and the excellent accuracy of the proposed method.

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