Real Time Detection and Automatic Detection of Images, Obstacles, Color, Distance by a Live Streaming Automatic Robot Using Raspberry Pi

2019 ◽  
Vol 11 (1) ◽  
pp. 49-53 ◽  
Author(s):  
Shweta Yadav ◽  
K. Bhanu Prasad ◽  
Ravi Shankar Singhal ◽  
Nivedita ◽  
Ushaben Keshwal
Author(s):  
G Sai Kiranmayi ◽  
B Bhanu ◽  
B Manikanta ◽  
N Ashok ◽  
G Govinda Raju

The main objective of this paper is to develop a virtual environment for detecting suspicious and targeted places for user without any loss of human life.The purpose of this project is to regulate robot with interface board of the raspberry pi,sensors and software to full fill real time equipment. There are sundry surveillance systems such as camera, CCTV etc. available in the market. In these systems, the person located in that particular area can only view what is transpiring in that place. We proposed a system to build an authentictime live streaming and monitoring system utilizing Raspberry pi with installed Wi-Fi connectivity. It can continuously monitor the objects. Robot can move in every direction (left, right,forward and backward). The webcam which is placed on the robotic unit will capture the video and it transmits vivacious to the remote end. The major application of this paper can be analysed utilizing HTML web page which can be acclimated to control the movement of the robot.


Author(s):  
Junyi Wang ◽  
Qinggang Meng ◽  
Peng Shang ◽  
Mohamad Saada

This paper focuses on road surface real-time detection by using tripod dolly equipped with Raspberry Pi 3 B+, MPU 9250, which is convenient to collect road surface data and realize real-time road surface detection. Firstly, six kinds of road surfaces data are collected by utilizing Raspberry Pi 3 B+ and MPU 9250. Secondly, the classifiers can be obtained by adopting several machine learning algorithms, recurrent neural networks (RNN) and long short-term memory (LSTM) neural networks. Among the machine learning classifiers, gradient boosting decision tree has the highest accuracy rate of 97.92%, which improves by 29.52% compared with KNN with the lowest accuracy rate of 75.60%. The accuracy rate of LSTM neural networks is 95.31%, which improves by 2.79% compared with RNN with the accuracy rate of 92.52%. Finally, the classifiers are embedded into the Raspberry Pi to detect the road surface in real time, and the detection time is about one second. This road surface detection system could be used in wheeled robot-car and guiding the robot-car to move smoothly.


2021 ◽  
Author(s):  
Bixen Telletxea ◽  
Mar Tapia ◽  
Marta Guinau ◽  
Manuel J. Royán ◽  
Pere Roig Lafon ◽  
...  

<p>Seismic sensors installed in areas prone to rockfalls provide a continuous record of the phenomenon, allowing real-time detection and characterization. Detection of small scale rockfalls (< 0.001 m<sup>3</sup>), that might be precursors of larger events, can be worthwhile for early warning systems of rockfalls. However, seismic signals are closely dependent on the characteristics of the event and on the geotechnical characteristics of the ground, making the detection of small rockfalls complex and requiring detailed in-situ analyzes. For this reason, an experiment was carried out on the UB experimental site (Puigcercós Cliff, Catalonia, NE Spain) on 6<sup>th</sup>-7<sup>th</sup> of June 2013, where 21 rocks with volumes ranging from 0.0015 m<sup>3</sup> to 0.0004 m<sup>3</sup> were thrown from the top of the cliff (200 m long and 27 m high) and the seismic signals were registered with three 3D short period seismic sensors located at different distances from the rock wall: 57 m, 67 m, and 107 m.</p><p>The recorded seismic signals have a frequency content between 10-30 Hz, and the duration of the peak amplitudes varied between 0.3 and 0.6 s. Based on these characteristics, different phases of the dynamics of the rockfalls were identified, including main impacts, rebounds, flights, rolling and final stop of the events. The furthest station recorded the lowest frequency and amplitude values, limiting our ability to detect those blocks smaller than 0.0015 m<sup>3</sup>. Comparing the results with the nearest station, seismic attenuation phenomena is detectable even at distances of 50 m.</p><p>After the experiment, a permanent seismic station was installed in the area, at 107 m from the cliff. Using LiDAR and 2D imagery monitoring, two naturally triggered rockfalls were identified on 30<sup>th</sup> and 31<sup>st</sup> August 2017 (0.28 m<sup>3</sup> and 0.25 m<sup>3</sup> respectively). Based on the results from the experiment and an automatic detection system, these main events and prior minor events have been found in the continuous seismic records of this permanent station. The characteristics of these natural detachments differ partially from the artificially triggered rockfalls during the experiment, since the geometry of the seismic signals is different. The observed shapes of the natural detachments are similar to that of granular flows, much more continuous than the sharp shapes that were observed in the isolated blocks of the experiment. This shows the possibility of incorporating seismic stations for the automatic detection and initial characterization of rockfalls and its effectiveness in detecting frequencies of occurrence.</p><p>In order to evaluate the possibility of estimating rockfall volumes, diverse energy ratios (<em>E<sub>s</sub>/E<sub>p</sub></em>) were calculated. However, precise volume estimation is not possible. Nevertheless, the combination of seismic data with LiDAR and photographic techniques allows accurate new volume calculations of rockfalls to be incorporated progressively into the study of rockfalls.</p><p>ACKNOWLEDGMENTS: The authors would like to acknowledge the financial support from CHARMA (CGL2013-40828-R) and PROMONTEC (CGL2017-84720-R AEI/FEDER, UE) projects, Spanish MINEICO. We are also thankful to Origens UNESCO Global Geopark.</p>


2018 ◽  
Vol 5 (5) ◽  
pp. 629
Author(s):  
Erfan Rohadi ◽  
Anastasia Merry Christine ◽  
Arief Prasetyo ◽  
Rosa Andrie Asmara ◽  
Indrazno Siradjuddin ◽  
...  

<p><strong>Abstrak</strong></p><p><strong><br /></strong>Teknologi <em>video surveillance system </em>atau kamera pengawas sudah menjadi alat yang sangat penting karena mayoritas kebutuhan masyarakat sekarang ini menginginkan informasi yang cepat untuk diakses serta praktis dalam penggunaannya. Dalam penelitian ini sebuah protokol H.264 dipergunakan dalam <em>memproses video streaming</em>pada <em>video surveillance system</em>yang berfungsi sebagai pengirim dan pengontrol paket data <em>streaming</em>dari kamera pengawas ke penerima yaitu sebagai <em>user video surveillance system</em>. Analisis <em>frame video</em>pada protokol H.264 dilakukan pada live streaming server berupa embedded system yang terintegrasi pada video <em>surveillance system</em>dengan kamera pengawas. Dari hasil uji coba menunjukan bahwa Protokol H.264 memberikan kompresi kualitas <em>video</em>yang baik, sehingga implementasi <em>Video Streaming</em>lalu lintas kendaraan ini menjanjikan dapat membantu memudahkan masyarakat dalam mendapatkan informasi dan juga mengetahui kondisi lalu lintas secara<em>realtime </em>serta efektif dan efesien. Implementasi <em>Video streaming</em>secara <em>realtime</em>ini memantau kondisi lalu lintas di suatu Lokasi dengan pendeteksi ketersediaan kamera CCTV <em>(Closed Circuit Television)</em>dan <em>Raspberry pi</em>sebagai <em>server</em>. </p><p> </p><p><em><strong>Abstract</strong></em></p><p><em><strong></strong></em><em><span>Technology of video surveillance system has become a very important tool because the majority of the needs of today's society want information that is fast to access and practical in its use. In this study an H.264 protocol is used in processing video streaming in video surveillance system that functions as a sender and controller of streaming data packets from surveillance camera to receiver that is as user video surveillance system. The frame video analysis of the H.264 protocol has performed on a live streaming server in the form of embedded systems integrated in video surveillance system with surveillance cameras As a result, the system shows that the H.264 protocol provides good video quality compression, so the implementation of Video Streaming traffic this vehicle promises to help facilitate the public in getting information and also know the real time traffic conditions as well as effective and efficient. Implementation streaming video in real time this monitor traffic conditions in a location with the detection of the availability of CCTV (Closed Circuit Television) and Raspberry Pi cameras as a server.</span></em></p>


Author(s):  
Ken Rudman ◽  
Mathieu Bonenfant ◽  
Mehmet Celik ◽  
Joe Daniel ◽  
Jaap Haitsma ◽  
...  

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1142
Author(s):  
Songyu Li ◽  
Håkan Lideskog

Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting.


2016 ◽  
Vol 125 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Ken Rudman ◽  
Mathieu Bonenfant ◽  
Mehmet Celik ◽  
Joe Daniel ◽  
Jaap Haitsma ◽  
...  

2012 ◽  
Author(s):  
Anthony D. McDonald ◽  
Chris Schwarz ◽  
John D. Lee ◽  
Timothy L. Brown

2015 ◽  
Vol 1 (1) ◽  
pp. 37-45
Author(s):  
Irwansyah Irwansyah ◽  
Hendra Kusumah ◽  
Muhammad Syarif

Along with the times, recently there have been found tool to facilitate human’s work. Electronics is one of technology to facilitate human’s work. One of human desire is being safe, so that people think to make a tool which can monitor the surrounding condition without being monitored with people’s own eyes. Public awareness of the underground water channels currently felt still very little so frequent floods. To avoid the flood disaster monitoring needs to be done to underground water channels.This tool is controlled via a web browser. for the components used in this monitoring system is the Raspberry Pi technology where the system can take pictures in real time with the help of Logitech C170 webcam camera. web browser and Raspberry Pi make everyone can control the devices around with using smartphone, laptop, computer and ipad. This research is expected to be able to help the users in knowing the blockage on water flow and monitored around in realtime.


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