Topic: haar-training Goto Github
Some thing interesting about haar-training
Some thing interesting about haar-training
haar-training,A complete collection of Haar-Cascade files. Every Haar-Cascades here!
User: anaustinbeing
haar-training,Auto Attendance System Using Real Time Face Recognition With Various Computer Vision & Machine Learning Tools
User: anupsarkar-dev
haar-training,Haar classifier to detect Bodna or Lota in images or Video.
User: ashraf-minhaj
haar-training,Detecta el plexo branquial desde imgenes de ultrasonido del cuello
User: gastonzarate
haar-training,Neural Network-male-female-recognition-human-skin-detection
User: neelgajjar
haar-training,An interactive visualization of Haar-like feature scaling
User: noahlevenson
haar-training,Viola-Jones face detection from scratch in WebAssembly
User: noahlevenson
haar-training,Image cropper used for preparing object samples for further Haar cascade training. Creates a samples of objects from original images.
User: overchenkodev
haar-training,The interface of the autonomous car with the surroundings must be similar to that of human way of interaction. Humans use their eyes as a source of vision and then processes the visual signals in his/her brain and takes the necessary action accordingly. Similarly the autonomous car uses a camera as a visual source to know its surrounding, path etc. and uses the image processing techniques on the images received from the camera. This processing takes place on a minicomputer (RASPBERRY PI). After image processing, control instructions are passed on to the driving motors which helps in steering the vehicle accordingly.
User: praveengadiyaram369
haar-training,The repository is a part of an experiment, where a Stereo camera sensor was developed for Object detection and distance calculation using machine learning with HAAR-CASCADE- Classifier for an Autonomous Car. The idea was to compare the accuracy of a Stereo camera with that of LiDar sensors to cut down the overall cost of the system.
User: rchoudhary6088
haar-training,System detects vehicle density at traffic junctions and allots the required time to traffic lights for vehicle passage dynamically. Classifier is trained using positive and negative images. Further, Adaptive Boosting is used to combine a number of weak classifiers into a strong one. The outcome of AdaBoost is Trained Cascade, which is finally used to detect vehicles in the images clicked.The system uses webcam to detect vehicles, count them and allow traffic passage according to traffic density of each lanes.
User: rishabh-sachdeva
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