Python tensorflow is used to predict trafffic congestion types based on image object recognition
Wildfire is a natural disaster, causing irreparable damage to local ecosystem. Sudden and uncontrollable wildfires can be a real threat to residents’ lives. Statistics from National Interagency Fire Center (NIFC) in the USA show that the burned area doubled from 1990 to 2015 in the USA. Recent wildfires in northern California (reported by CNN) have already resulted in more than 40 deaths and 50 missing. More than 200,000 local residents have been evacuated under emergency. The wildfires occur 220,000 times per year globally, the annual burned area is over 6 million hectares. Accurate and early detection of wildfire is therefore of great importance. Fire detection task is crucial for people safety. Several fire detection systems were developed to prevent damages caused by fire. One can find different technical solutions. Most of them are sensors based and are also generally limited to indoors. They detect the presence of particles generated by smoke and fire by ionization, which requires a close proximity to the fire. Consequently, they cannot be used in large covered area. Moreover, they cannot provide information about initial fire location, direction of smoke propagation, size of the fire, growth rate of the fire, etc. To get over such limitations video fire detection systems are used
:art: Color recognition both on a webcam stream in real-time, on video and on a single image using K-Nearest Neighbors Machine Learning classification algorithm is trained with Color Histogram Features.
Spatio-Temporal Deep Neural Network Based Video Smoke Detection
FPGA Deployable Fire Detection Model for Real-Time Video Surveillance Systems Using Convolutional Neural Networks
Object Detection algorithm trained to identify litter from Google Streets :: [CIS490 Project Management Final Project]
