Video Object Detection

To enhance your smartphone's video capabilities with AI-powered detection, check out Motion Detection for Android. This app detects every movement and saves videos automatically to either your phone or cloud server. With its smart detector that only starts recording when motion is detected, the app is both efficient and convenient.

For even more advanced features, consider using Video Surveillance Cloud, a hybrid cloud solution that employs real-time object recognition video analytics on the camera stream source side. With this technology, you can access your surveillance footage remotely from anywhere. The software provides features such as online security monitoring, object detection, motion detection, event-triggered and time-lapse recording, remote viewing, facial recognition, and automated license plate recognition.

Object detection software has revolutionized many industries, including video surveillance, self-driving cars, and robotics. One of the most common uses of object detection is to detect humans in an image or video. This article will explore how object detection software can detect humans and provide some background theory on the topic.Object detection software works by identifying objects in an image or video and classifying them into different categories. These categories can include cars, animals, and, in our case, humans. The software proposes the objects it identifies as belonging to a certain class, humans in this case, using a probability score. It then defines the boundaries of the proposed humans with x-y origins and height and length values.To detect humans, object detection software uses deep learning artificial intelligence systems that process a vast number of images to learn to recognize humans. These processing blocks, called models, are trained on millions of images that include humans in various positions, backgrounds, and lighting conditions. The models learn to detect patterns that are indicative of human features, such as head and body shapes, clothing, and facial features.The models used for human detection are typically based on convolutional neural networks (CNNs). CNNs are a type of artificial neural network that are designed to process images. They work by applying a series of filters to an image to extract features that are relevant to the task at hand. In the case of human detection, the filters are designed to recognize patterns that are indicative of human features.Once the object detection software has detected humans in an image or video, the results can be used in various ways. For example, in video surveillance, the software can be set up to trigger an alarm or send an alert when humans are detected in a certain area. In self-driving cars, the software can be used to detect pedestrians and avoid collisions. In robotics, the software can be used to recognize humans and interact with them in a more natural way.In conclusion, detecting humans in an image or video using object detection software is a complex process that involves deep learning artificial intelligence systems and convolutional neural networks. The software identifies humans based on patterns that are indicative of human features, such as head and body shapes, clothing, and facial features. The results of human detection can be used in various applications, such as video surveillance, self-driving cars, and robotics.

The ultimate goal of computer vision is to enable machines to recognize objects, understand scenes, and interact with the world around them based on visual inputs. This technology is used in a wide range of applications, including image and video processing, object recognition, face recognition, autonomous vehicles, medical image analysis, and many more. It combines computer science, mathematics, physics, and artificial intelligence to provide machines with the ability to see and interpret the visual world.

Finding objects in digital images, photos and videos. Searching by image - using images as search criteria. Counting and measuring applications (parts, cars, people, metal sheets, pills, grain, birds, ..). Tracking moving objects in video. Facial recognition, head tracking, face tracking and analysis, Human Action Recognition. Video Content Analysis