About the Project The project is about Human dectection. Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. Detecting human beings accurately in a visual surveillance system is crucial for diverse application areas including abnormal event detection, human gait characterization, congestion analysis, person identification, gender classification and fall detection for elderly people. Human detection and tracking are tasks of computer vision systems for locating and following people in video imagery. Human detection is the task of locating all instances of human beings present in an image, and it has been most widely accomplished by searching all locations in the image, at all possible scales, and comparing a small area at each location with known templates or patterns of people.
What is OpenCV? OpenCV-Python is the Python API of OpenCV. It combines the best qualities of OpenCV C++ API and Python language. Currently OpenCV supports a wide variety of programming languages like C++, Python, Java etc and is available on different platforms including Windows, Linux, OS X, Android, iOS etc. Also, interfaces based on CUDA and OpenCL are also under active development for high-speed GPU operations.
Video detection is based on real-time image processing providing efficient wide-area detection well suited for registration of incidents on roads and in tunnels. Connected to Traffic Controllers, the application can also be used for vehicle detection at signalized intersections where it is difficult or expensive to install inductive loops. Video-detection systems are also considered nonintrusive.
Video detection combines real-time image processing and computerized pattern recognition in a flexible platform; it uses a vision processor to analyze real-time changes in the image. In this system, cameras called image sensors capture images and provide a video signal to the vision processor. The video signal is analyzed and the results are recorded. Video image detection is one of the leading alternatives to the commonly used loop detectors. It is progressively being used to detect traffic intersections and interchanges. This is because video detection is often cheaper to install and maintain than inductive loop detectors at multilane intersections.