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Machine learning of visual recognition relates to patterns and their classification. [5] [6] True video analytics can distinguish the human form, vehicles and boats or selected objects from the general movement of all other objects and visual static or changes in pixels on the monitor. It does this by recognizing patterns. When the object of ...
Small object detection is a particular case of object detection where various techniques are employed to detect small objects in digital images and videos. "Small objects" are objects having a small pixel footprint in the input image. In areas such as aerial imagery, state-of-the-art object detection techniques under performed because of small ...
Objects detected with OpenCV's Deep Neural Network module (dnn) by using a YOLOv3 model trained on COCO dataset capable to detect objects of 80 common classes. Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. [1]
Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. It has a variety of uses, some of which are: human-computer interaction, security and surveillance, video communication and compression, augmented reality, traffic control, medical imaging [1] and video editing.
Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision, and specifically object detection and localization. [1] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the category (e.g. car or ...
Anomaly detection has become increasingly vital in video surveillance to enhance security and safety. [13] [14] With the advent of deep learning technologies, methods using Convolutional Neural Networks (CNNs) and Simple Recurrent Units (SRUs) have shown significant promise in identifying unusual activities or behaviors in video data. [13]
All detection techniques are based on modelling the background of the image, i.e. set the background and detect which changes occur. Defining the background can be very difficult when it contains shapes, shadows, and moving objects. In defining the background, it is assumed that the stationary objects could vary in color and intensity over time.
Motion detection: Motion detection is used to determine the presence of relevant motion in the observed scene. Shape recognition: Shape recognition is used to recognize shapes in the input video, for example circles or squares. This functionality is typically used in more advanced functionalities such as object detection. Object detection