Traditional methods of computing have never matched the human ability to see and recognize objects with ease and precision, but with recent advancements of neural network image classifying localization and algorithms, computers are coming ever closer to a fully developed computer vision. Identifying objects is not the sole problem; being able to quickly and efficiently classify objects in real time is the true challenge. The focus of this paper will be to detail an efficient algorithm to classify objects in front of a camera and to explore the various applications of object detection in real-time.
Learning is a multifaceted phenomenon that involves the acquisition and labeling of data by means of pattern recognition. On the contrary, computers function on a system of 'true" and 'false", on and off. This system of black or white is predisposed to problems when presented with the challenge of implementing this system of capabilities in computers. The technique of modeling the learning process in computers is called machine learning, and it will be the subject of our quest to define and explore an algorithm that is able to detect and classify unknown objects.
The trajectory of technology in the 21st century is for autonomous machines to assume jobs traditionally performed by humans. In relation to the matter of object detection, this manifests itself in three main areas:
Video Analysis- The development of learning systems that have the capacity to analyze video for the purposes of further processing.
Cognitive Simulation- The investigation and the computer simulation of the human learning and seeing process. Computer-Environment Interaction- The ability for the computer to react to its environment through computer vision. Although many research efforts strive to advance solely one of these objectives, progress toward one inevitably leads to progress in others. However, in previous years machine learning algorithms were marred by premature publicity from large companies and progress was slowed in favor of hastily implemented technologies that failed to advance the world in any meaningful way. Despite this setback, the world of object detection will continue to advance ever more as the meaningless intensive promotion of a technology has largely been viewed in the past as the precursor to rapid meaningful progress in the future.
Audio and visual analysis, by far the largest section of real-time computer vision with machine learning, is the most vital and extensive area of the subject which ranges from automatic inspection to image and video restoration. This technology is currently being used in cutting edge systems such as confirming accuracy in circuit boards, unmonitored customer service calls, visual surveillance and security, and face detection. However, researchers in computer vision and object detection have been further developing mathematical techniques for observing and processing the three-dimensional shape and appearance of objects in imagery. With these new reliable techniques, autonomous computer vision no longer can just replace the rudimentary jobs performed by humans, but surpass the human ability to detect and observe the physical world. Given the significance of personalized medicine and the growing trend of machine learning techniques, the use of object detection to assist medical personnel with the diagnosis of cancerous tumors in patients is starting to appear. Systematic object detection models that include mixed data such as clinical, genomic, and visual, have shown a success rate of up to 97% allowing for patients to be treated earlier and more effectively. Such technology in an increasing scope of fields will be crucial to the facilitation of autonomy throughout the world.
The brain is a highly evolved system which is capable of learning, memory retention, pattern recognition, and visual analysis in an efficient way. In order to accomplish this, the brain employs a self adapting mechanism for change that reacts to your sensory functions. These processes function by finding patterns and creating a model of the world around you. When new, unknown stimulants are received, the brain references this model to predict what objects are and how they move throughout space in relation to itself. This modeling behavior in a controlled and repeatable environment has proven challenging to researches for many decades. Although, with cutting edge machine learning tools, it is now possible to replicate this kind of learning on a computer. With continual improvement on the computer models used, the ability to develop a more full understanding of the brain and its functions nears. Furthermore, object detection and analysis can be used not only to model the functions of the brain, but also monitor and inspect cognitive behavior. Analyzing these signals in real-time is a major challenge. Recently, these discussed models and techniques successfully contribute to this exciting field, and allows for more advanced consumer technologies in the future. '(a) new insights into general mental state monitoring can be gained and (b) brain“computer interfacing becomes feasible without the need for subject training." (Klaus-Robert Muller 88).
Currently, instructing a computer to perform a task requires constructing a complete and correct labor intensive algorithm, and then arduously programming that algorithm into the computer. Common computer systems do not have the ability to truly learn to perform a task through examples or by analogy to a similarly completed task. Nor can they significantly improve based on past mistakes. Machine learning, and by extension object detection, strives to open up the possibilities of instructing computers in these new ways. The explosion of new applications for object detection has many possibilities with applications that are not entirely known to engineers today.
The rapid development of groundbreaking technology in the machine learning field has enabled engineers to program computers to complete tasks traditionally performed by humans. In the past few years, the abilities of object detection technology has far exceeded expectations. This major breakthrough will soon allow tech companies to use computers to innovate based on patterns that the learning system has previously picked up on. The future of technology is no longer one where computers are the platform used to innovate, but rather a world in which computers perform the innovation themselves. This creates a level of efficiency currently unattainable with the human labor model.
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