Bike Tracking System With Dead Man Alert > 자유게시판

Security for bike riders is a must, with road bike or mountain bike accidents occurs and as soon as doable, emergency personnel should find you. That is much more important when you journey alone. As bike consumer, I know how vital is security for bikers and I want to construct a simple, low energy and excessive coverage device to monitor the rides and have a security system in case of an accident. Because of the large coverage of sigfox network in Spain and an Arduino MKRFOX1200 with an IMU and a GPS module, it is possible to monitor iTagPro portable the biker and in case the bike falls and it's on the ground for greater than a configured time, the devide sends and alarm with the GPS coordinates. This system checks every a configured timer (for instance 10 seconds) the bike the inclination in the three axis (X, Y and Z) with the IMU.

This database is checked by an Arduino Leonardo ETH with an Arduino GSM Shield 2 and sends SMS to someone telephone you want to be warned in case os accident. Also, this gadget sends the GPS place or IMU knowledge every a configured timer, for example 10 minutes. In the final version, the system is placed in the bottom of the bike bottle holder. This demo video reveals that about 15 seconds lapse since the system detects the bottle fall until a SMS is received to the configured phone number. Also, this machine might be used for monitoring your bike rides or be tracked by your family or pals, or in case your bike is stolen, the bike will probably be positioned in a short while. As an anti-theft device might be hidden contained in the bike frame sooner or later. Another application for this system might be for contributors tracking in bike competitions with each options security and dwell monitoring for organizers. Additionally, with a low vitality bluetooth, this machine might be controlled by a smartphone and iTagPro portable add more options. For example send your location to your bike group to know how far you're form them.

Object detection is widely used in robotic navigation, clever video surveillance, industrial inspection, aerospace and plenty of different fields. It is a vital department of image processing and computer vision disciplines, and can also be the core part of clever surveillance techniques. At the same time, target detection is also a basic algorithm in the field of pan-identification, which performs a vital function in subsequent tasks such as face recognition, gait recognition, crowd counting, and instance segmentation. After the primary detection module performs target detection processing on the video frame to acquire the N detection targets within the video frame and the primary coordinate information of each detection goal, the above methodology It additionally includes: displaying the above N detection targets on a display. The first coordinate info corresponding to the i-th detection goal; obtaining the above-talked about video frame; positioning within the above-talked about video frame in line with the primary coordinate info corresponding to the above-talked about i-th detection goal, obtaining a partial image of the above-talked about video body, and figuring out the above-mentioned partial image is the i-th picture above.

The expanded first coordinate data corresponding to the i-th detection target; the above-mentioned first coordinate information corresponding to the i-th detection target is used for positioning in the above-talked about video body, together with: according to the expanded first coordinate information corresponding to the i-th detection goal The coordinate data locates in the above video frame. Performing object detection processing, if the i-th picture consists of the i-th detection object, acquiring position info of the i-th detection object in the i-th image to obtain the second coordinate information. The second detection module performs goal detection processing on the jth image to find out the second coordinate information of the jth detected goal, the place j is a positive integer not higher than N and not equal to i. Target detection processing, acquiring a number of faces within the above video frame, and first coordinate information of each face; randomly obtaining target faces from the above multiple faces, and intercepting partial images of the above video body based on the above first coordinate info ; performing goal detection processing on the partial image via the second detection module to obtain second coordinate info of the target face; displaying the goal face in accordance with the second coordinate information.