Bike Tracking System With Dead Man Alert

Security for bike riders is a must, with highway bike or mountain bike accidents occurs and as soon as possible, iTagPro smart tracker emergency personnel must find you. This is even more vital whenever you trip alone. As bike person, I know how important is security for bikers and I would like to construct a easy, low power and high coverage device to observe the rides and have a security system in case of an accident. Because of the massive coverage of sigfox network in Spain and an Arduino MKRFOX1200 with an IMU and iTagPro smart tracker a GPS module, it's potential to watch the biker and in case the bike falls and it is on the ground for greater than a configured time, the devide sends and alarm with the GPS coordinates. This gadget checks every a configured timer (for instance 10 seconds) the bike the inclination within 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 any person cellphone you want to be warned in case os accident. Also, this device sends the GPS position or IMU knowledge every a configured timer, for instance 10 minutes. In the ultimate model, the machine is placed in the bottom of the bike bottle holder. This demo video shows that about 15 seconds lapse because the system detects the bottle fall till a SMS is obtained to the configured phone quantity. Also, this gadget could be used for monitoring your bike rides or be tracked by your family or buddies, or in case your bike is stolen, the bike will be situated in a short time. As an anti-theft system may very well be hidden contained in the bike frame in the future. Another utility for this machine could be for members monitoring in bike competitions with each features safety and stay monitoring for organizers. Additionally, with a low power bluetooth, this system could possibly be controlled by a smartphone and add extra features. For instance ship your location to your bike group to know how far you're form them.

Object detection is broadly utilized in robotic navigation, intelligent video surveillance, industrial inspection, aerospace and lots of other fields. It is a vital department of image processing and laptop vision disciplines, and can be the core part of intelligent surveillance systems. At the identical time, goal detection is also a fundamental algorithm in the sphere of pan-identification, which performs a vital role in subsequent duties resembling face recognition, gait recognition, crowd counting, and instance segmentation. After the primary detection module performs goal detection processing on the video frame to acquire the N detection targets in the video frame and the first coordinate information of every detection goal, the above methodology It also includes: displaying the above N detection targets on a display. The primary coordinate data corresponding to the i-th detection goal; acquiring the above-talked about video frame; positioning within the above-talked about video body according to the primary coordinate information corresponding to the above-mentioned i-th detection goal, obtaining a partial picture of the above-talked about video body, and determining the above-talked about partial image is the i-th picture above.

The expanded first coordinate data corresponding to the i-th detection goal; the above-talked about first coordinate info corresponding to the i-th detection target is used for positioning within the above-mentioned video frame, including: in line with the expanded first coordinate info corresponding to the i-th detection goal The coordinate info locates within the above video body. Performing object detection processing, if the i-th picture contains the i-th detection object, acquiring place information of the i-th detection object in the i-th picture to obtain the second coordinate data. The second detection module performs target detection processing on the jth picture to find out the second coordinate data of the jth detected target, where j is a optimistic integer not better than N and never equal to i. Target detection processing, acquiring a number of faces in the above video body, and first coordinate data of every face; randomly obtaining goal faces from the above multiple faces, and intercepting partial photographs of the above video frame according to the above first coordinate information ; performing goal detection processing on the partial picture by the second detection module to acquire second coordinate information of the goal face; displaying the goal face in accordance with the second coordinate data.