This approach is not suitable for cameras mounted on moving objects (e.g., cars) or Pan-Tilt-Zoom cameras.Īdditionally, image size and scaling factors must be the same across all cameras and we must be able to access still images from each camera. That is, the cameras are fixed and are all watching the same geo-region. The approach outlined in this post is suitable for camera systems where cameras are observing a fixed field-of-view (FoV). Equal time and manpower must be spent on each camera, which can be excessive in some cases. No uniform or simple way to automate the process exists. Placement of checkerboards requires populated and frequently active areas to be cleared for calibration work, impractical on public roads and in other crowded spaces. The technique requires creation of custom checkerboards for each application and the checkerboards must be placed at various angles in the camera view. Specifically, the checkerboard approach is: This makes it impractical for smart cities applications like parking garages and traffic intersections that regularly employ hundreds of cameras in concert. While the checkerboard approach is a high-fidelity method for calibration, it’s both labor and resource intensive. From there, a homomorphic transformation (translation from image plane to real-world) can be used to infer global coordinates. One way to do this is to use a “checkerboard” pattern to infer the camera parameters. Global coordinates are then inferred with a simple geometric transformation from camera world to the real world. Several popular methods use a process based on inferring the intrinsic and extrinsic camera parameters. Multiple approaches exist for calibrating cameras to yield global coordinates. Let’s take a closer look at how to approach calibration for applications built using DeepStream 3.0. Solutions that require multi-camera object tracking, movement summarization, geo-fencing, and other geo-locating for business intelligence and safety can leverage the same technique. Transformations like this are critical to a variety of use cases beyond simple visualization. Technically, this is a transformation from the image plane of the camera (image of the car) to a global geo-location (latitude/longitude coordinate). The car would ideally be placed in an information grid that also projects a live bird’s eye view of the activities in the city for the operator’s use.ĭoing this means translating that camera image into latitude and longitude coordinates corresponding to the car’s location on that intersection. When the camera sees a car, the raw image of the car isn’t useful to a smart cities system on its own. One of the big issues with extracting usable data from video streams is taking an object detected by the camera and translating it into a geo-location. This approach to calibration is meant for complex, scalable environments like these, and does not require a physical presence at the site. The DeepStream SDK is often used to develop large-scale systems such as intelligent traffic monitoring and smart buildings. This post walks through the details of calibration using DeepStream SDK 3.0. Calibration is a key step in this process, in which the location of objects present in a video stream is translated into real-world geo-coordinates. DeepStream exists to make it easier for you to go from raw video data to metadata that can be analyzed for actionable insights. Seeing another airliner flying head-on in real life or passing a slower aircraft is amazing.DeepStream SDK 3.0 is about seeing beyond pixels. And then for long hauls you have things like step climbs depending on weight, adverse weather systems along the path and ETOPS restrictions to complicate things for the AI aircraft's profiles. That is however never the case in real life. If it could be solved I guess you could call the dispatcher profession obsolete Not knowing the actual flight plan but the take-off and landing times according to AFRE, even considering no enroute winds, would result in aircraft flying a lot slower than in real life if you assume a great cycle track. The absence of knowing all specific flightplans the aircraft are taking in real life plus enroute conditions makes this a humongous task. A lot left to do with P2A integration and improving collision avoidance but something to think about. I want my contrails Laminar!!! *shakes fist*). It somehow looks at the loaded flight schedules and makes an educated guess about where an AI plane would be on it's journey and if it's flight plan takes it near the player's craft it will spawn them in at about the 40nm mark and ZOOM, you see traffic passing under you, over you, in front of you, mighty contrails billowing behind them (which we don't get in X-Plane. I don't know how FSX/P3D is able to do it but you get enroute traffic all the time. Are there any plans in the future to get Enroute traffic up and running? This is the final missing piece of the AI Traffic puzzle.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |