Detected-with-YOLO--Schreibtisch-mit-Objekten

Object detection is a computer technology related to

computer vision Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human ...

and

image processing An image is a visual representation of something. It can be two-dimensional, three-dimensional, or somehow otherwise feed into the visual system to convey information. An image can be an artifact, such as a photograph or other two-dimensio ...

that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. Well-researched domains of object detection include

face detection Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images. Face detection also refers to the psychological process by which humans locate and attend to faces in a visual scene. ...

and pedestrian detection. Object detection has applications in many areas of computer vision, including image retrieval and video surveillance.

Uses

Computer_vision_sample_in_Simón_Bolivar_Avenue,_Quito

It is widely used in

tasks such as image annotation, vehicle counting,

activity recognition Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several c ...

face recognition A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces. Such a system is typically employed to authenticate users through ID verification services, an ...

, video object co-segmentation. It is also used in tracking objects, for example tracking a ball during a football match, tracking movement of a cricket bat, or tracking a person in a video.

Concept

Every object class has its own special

features Feature may refer to: Computing * Feature (CAD), could be a hole, pocket, or notch * Feature (computer vision), could be an edge, corner or blob * Feature (software design) is an intentional distinguishing characteristic of a software ite ...

that helps in classifying the class – for example all

circle A circle is a shape consisting of all points in a plane that are at a given distance from a given point, the centre. Equivalently, it is the curve traced out by a point that moves in a plane so that its distance from a given point is cons ...

s are round. Object class detection uses these special features. For example, when looking for circles, objects that are at a particular distance from a point (i.e. the center) are sought. Similarly, when looking for squares, objects that are

perpendicular In elementary geometry, two geometric objects are perpendicular if they intersect at a right angle (90 degrees or π/2 radians). The condition of perpendicularity may be represented graphically using the '' perpendicular symbol'', ⟂. It c ...

at corners and have equal side lengths are needed. A similar approach is used for face identification where eyes, nose, and lips can be found and

like skin color and distance between eyes can be found.

Methods

Comparison of speed and accuracy of detectors

Methods for object detection generally fall into either neural network-based or non-neural approaches. For non-neural approaches, it becomes necessary to first define features using one of the methods below, then using a technique such as

support vector machine In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. Developed at AT&T Bell Laborat ...

(SVM) to do the classification. On the other hand, neural techniques are able to do end-to-end object detection without specifically defining features, and are typically based on

convolutional neural network In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of artificial neural network (ANN), most commonly applied to analyze visual imagery. CNNs are also known as Shift Invariant or Space Invariant Artificial Neural Netwo ...

s (CNN). * Non-neural approaches: ** Viola–Jones object detection framework based on Haar features ** Scale-invariant feature transform (SIFT) ** Histogram of oriented gradients (HOG) features * Neural network approaches: ** Region Proposals (R-CNN, Fast R-CNN, Faster R-CNN, cascade R-CNN.) ** Single Shot MultiBox Detector (SSD) ** You Only Look Once (YOLO) ** Single-Shot Refinement Neural Network for Object Detection (RefineDet) ** Retina-Net ** Deformable convolutional networks

References

* * {{cite web , url=http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en , title=ETHZ – Computer Vision Lab: Publications , publisher=Vision.ee.ethz.ch , access-date=2013-10-09 , archive-url=https://web.archive.org/web/20130603170209/http://www.vision.ee.ethz.ch/publications/get_abstract.cgi?procs=560&mode=&lang=en , archive-date=2013-06-03 , url-status=dead

External links

Multiple object class detection

Spatio-temporal action localization

Video object detection and co-segmentation
Object recognition and categorization Surveillance Applications of computer vision Gesture recognition

Uses

Concept

Methods

See also

References

External links