Welcome to the frontier of technology, where the giants of Silicon Valley vie to turn science fiction into reality. For years, ambitious companies like Google and Microsoft have been venturing into spatial computing, each with varying degrees of success. For instance, Google Glass and Microsoft HoloLens have had their moment in the limelight, but neither fully captured the mainstream market. Now, enter Apple with its Vision Pro. This device promises to redefine our interaction with technology and usher in the long-anticipated era of spatial computing.
But what sets Apple’s Vision Pro apart from its predecessors? While previous attempts at spatial computing were commendable for their pioneering spirit, they often stumbled over issues such as user experience, aesthetic design, and limited practical applications. Apple seems to have learned from these lessons with a device that offers sleek design, an intuitive user interface, and a robust ecosystem of applications that could finally bring spatial computing into the mainstream.
Computer Vision Helps Organizations Connect The Digital And Physical Worlds
Devices such as Apple’s Vision Pro use several technologies to mix the digital and physical worlds. One of them is computer vision (CV). Of course, computer vision has wider usage beyond headsets like Apple’s Vision Pro, growing from a niche field to a cornerstone of AI advancement across industries. The ability to automate human sight with computers opens massive opportunities and use cases across every sector, including manufacturing, healthcare, insurance, transportation, smart cities, agriculture, sports, retail, and logistics. Falling costs — driven by higher computational efficiency, decreasing hardware costs, and new technologies such as model compression, low-code/no-code, and automation — have made CV applications economically feasible, further accelerating adoption. The following trends are accelerating the adoption of CV applications:
- Organizations are keen to use CV to innovate and increase operational efficiency. For example, Tyson Foods, which processes 40 million chickens per week, uses CV to automatically count chicken trays on production lines, boosting efficiency and accuracy in its operations.
- Pretrained visual foundation models lower the barriers to entering the CV market, spurring many new solutions. For example, TensorFlow Model Garden is a repository that offers a diverse set of state-of-the-art CV models pretrained on large datasets. The TensorFlow team maintains it and provides a solid foundation for training and fine-tuning CV models to meet specific needs without starting from scratch.
- Advances in deep learning algorithms and hardware improve CV performance. Deep learning algorithms for CV have revolutionized how images are analyzed in the medical field, particularly with convolutional neural networks. These advancements have enabled high accuracy in tasks such as tumor detection and segmentation in imaging modalities like CT and MRI scans.
- Edge computing and on-device processing remove key adoption obstacles. For example, when using CV-based methods for construction site monitoring, internet-connected cameras must transmit large quantities of high-quality data to the central office, which may be thousands of miles away. Processing video data on edge devices significantly reduces the latency and bandwidth costs of sending video to distant servers.
Our newly published report, The State Of Computer Vision Technology, 2024, illustrates why organizations must continue pushing the boundaries of this technology while ensuring its responsible and ethical implementation.
But how exactly does one do that, especially when AI skills, including CV skills, are scarce? Luckily, the CV market has evolved significantly to help users address some of the implementation challenges. CV solutions that vendors provide today allow teams with little to no CV expertise to add the capacity to interact with visual input easily. In areas like barcode scanning, integrating a CV tool is as simple as adding one line of code to an application. At the same time, CV users continue to tackle more advanced challenges and create more complex and nuanced applications and experiences. Improvements to CV tools have benefited applications at the edge, such as via a self-checkout camera, along with CV applications that are only available online, like visual search. The Forrester Wave™: Computer Vision Tools, Q1 2024, will assist you in selecting the right partners for your CV-related needs.
Forrester Can Help You Navigate This Exciting Future
Overall, the future of CV technology looks exciting. As technology advances and the world changes, industries must adapt, and we at Forrester stand ready to help with a wide range of research on CV that can assist you in shaping your future.
Forrester clients can download The State Of Computer Vision Technology, 2024, for our take on CV technology — its various use cases across major industries, potential, and relative maturity. They can also connect with me through an inquiry or guidance session to discuss computer vision technology and any associated topics.
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