What are the top computer vision applications for AI in the next 5 years?

Here are some expected applications of computer vision: 1. Robotics vision 2. Self driving car 3. Image/video generation 4. Multi modal AI (along with text or other data sources) 5. Satellite image analysis and mapping

The top computer vision applications for AI in the next five years are expected to include advancements in autonomous vehicles, with improved perception and safety features. Healthcare will benefit from AI-driven medical image analysis, aiding in disease detection and diagnosis. Enhanced security and surveillance systems will use computer vision for threat detection and tracking. Augmented reality (AR) and virtual reality (VR) experiences will become more immersive with better object recognition and interaction. Retail will use AI to personalize shopping experiences and streamline checkout processes. Additionally, industrial automation will rely on computer vision for quality control and efficient manufacturing.

Self-driving cars have been getting better with computer vision. Current technology can already prevent 90%+ of the accidents and save lives. However, current state of vision software still cannot operate well in chaotic environments (imagine driving in Istanbul or Bangalore) as well as difficult conditions, such as snowy weather. In the next 5 years, I think advancements will likely come through the accumulation of huge amounts of data (Tesla is ahead of the game), advances in computational power, the growing complexity of models and new possibilities created by deep learning architectures. The goal is to get AI to adapt and react as quickly as the human brain.

Currently, global cars implement advanced driver-assistance systems (ADAS) at levels 2 & 3, featuring crash & lane alerts, speed control, and automatic parking. Ongoing advancements by various companies are pushing the boundaries of self-driving technology. As we aim for level 5 autonomy in ADAS, where vehicles can fully self-drive, computer vision emerges as a pivotal technology. It enables the perception of the environment by detecting lanes, signs, traffic lights, pedestrians, & vehicles. Crucially, CV is instrumental in planning and executing actions like steering, braking, & obstacle avoidance. However, challenges, including technical reliability and scalability, alongside regulatory complexities to the adoption of self-drive cars.

There's currently an innovation gap compared to advancements in natural image vision models. However, immense progress is seen, especially a paradigm shift in image generation using latent diffusion models. This shift holds potential to enable more intricate image generation, utilizing other modalities, such as medical reports or textual descriptions for image generation. There's a recent trend in the increased research and development of large vision models. These may address the scarcity of images in certain areas of healthcare and biomedical imaging, especially in situations where generating images from experiments or real cases is limited. The future of computer vision models for medical imaging looks very promising

Recently, a LinkedIn ad caught my attention—promoting a CPR Kit using medical imaging spectacles for precise first aid. While this showcases a practical application, it's crucial to acknowledge the broader impact of computer vision along with augmentation in medical imaging. Techniques like segmentation, classification, and detection significantly enhance accuracy. Beyond aiding first aid, computer vision contributes to diagnosis, treatment, and innovative applications like disease prediction and drug discovery. However, integration requires careful validation, standardization, and ethical considerations to ensure the quality and reliability of medical imaging technologies. Ref: https://tinyurl.com/y4b7ke9e

In the next 5 years, Augmented Reality (AR) will see significant growth, thanks to computer vision and AI: 1. Immersive Education: AR will enhance education by providing engaging and immersive learning experiences for students. 2. Enhanced Retail: It will revolutionize the retail industry, offering virtual product try-ons and bridging the online-offline shopping experience. 3. Medical Advancements: In healthcare, AR will aid surgeons in complex procedures and improve diagnostics, leading to more precise treatments.

You can slash latency with the following: 1. Turbocharged data processing, 2. Harmonize compatibility through standardized protocols, 3. Craft user-centered designs for intuitive use, etc. These are just a few ways we can solve some of the challenges.

- Among the top applications, video analytics stands out as a pivotal player. - For instance, in the retail industry, computer vision can analyze customer behavior in stores, optimizing product placements and enhancing the overall shopping experience. - However, it's crucial to acknowledge the challenges, including the need for substantial data, computational power, and privacy considerations. - As we navigate this exciting frontier, the synergy between computer vision and video analytics promises a paradigm shift in how we perceive and interact with the visual world. #ComputerVision #AIApplications #VideoAnalytics #FutureTech #InnovationInAI

Some of the most impactful Computer Vision use case in this area are around safety and green. Example: harsh driving behaviors allowing to avoid major crashes, preventing carbon emissions through route optimization, temperature regulation of perishable medical supplies and allowing safe deliveries 📦 Some of the widely used algorithms for these use cases include YOLOv3, YOLOv7, Mask R-CNN. COCO datasets are popular image datasets used for pre-training.

Integrating Generative AI with Computer Vision revolutionizes travel, offering a seamless, personalized journey. In planning, it crafts customized itineraries and virtual tours, making preparation immersive. During trips, real-time translation and landmark information enrich cultural experiences, while personalized recommendations enhance exploration. Post-travel, these technologies help create captivating narratives and organize photos, maintaining excitement for future adventures. This synergy transforms travel into a deeply personalized, enriching experience from start to finish.

A great question to think about is how computer vision will change in the next five years. I'm especially interested in how it can help people who can't "see". It would be really helpful if the technology is applied to develop a device with computer vision to "see" and sense their surroundings.