Jan 2020 | 92 pages
Get up-to-speed on the commercial readiness of state-of-the-art event-based vision systems across the globe
Rise in the scope and opportunity for event-based vision systems
Analysis of over 100 active entities working on event-based vision systems across imaging, automotive, semiconductor, electronics and software, as well as, research labs and universities
Analysis of 200+ patents from 2010 to 2019
10 companies, startups, and research institutes working on event-based cameras, including established firms such as Samsung and Sony
Seven major challenges with event-based cameras targeted by patenting entities
Top event-based vision system patentees for automotive-specific applications
Top event-based vision system patentees in the semiconductor industry
Global competitive landscape
Key partnerships and alliances in developing powerful event-based vision systems and their future roadmaps
Crucial insights pertaining to 8 major ongoing and closed projects in the domain
Present computer vision systems are highly reliant on frame-based approaches for capturing objects in motion. This approach produces large amounts of data, increasing the overall transmission and computation time. Event-based vision systems overcome this challenge. Unlike traditional cameras that capture frames at a fixed rate, they respond to pixel-level brightness changes. This makes event cameras operate with low latency and be more power efficient and sensitive to light. These advantages make them more suitable for object tracking, pose estimation, 3D reconstruction of scenes, depth estimation, feature tracking, and other perception tasks required by connected devices.
Latest AI-driven advancements in computer vision are enabling more human-like vision sensor systems. Also known as a neuromorphic, event-based vision, or dynamic vision sensor (DVS) camera, these systems have the potential to transform the computer vision landscape through reduced latency and low power consumption features. Their application areas include autonomous vehicles (for lower latency, HDR object detection, and low memory storage needs), robotics, IoT (for low power, always on devices), augmented reality/virtual reality (AR/VR) (low power and low-latency tracking), and other industrial automation use cases.
This report details the state-of-the-art event-based vision systems that have the potential to transform the traditional vision sensing architectures. It describes how event-based vision systems are finding applications across sectors and their potential to replace the frame-based solutions in critical, real-time applications. Get a deeper understanding of the computer vision ecosystem through the lens of emerging event-based vision systems and opportunities for investments and partnerships. Additionally, it includes a detailed analysis of relevant patents to help you develop IP strategies related to event-based vision system for automotive applications.
This report also asesses the challenges involved in the adoption of event-based vision systems, the solutions and approaches that the active participants are developing for introducing innovative products. The report combines a comprehensive analysis of patent filings, companies active in the space, and R&D activities from universities and research labs across the world, delivering key insights into the maturity and evolution of the technology.
2. Methodology of the Study
3. Entities Active in the event-based vision system
4. Patent Trend Analysis
5. Competitive Landscape
7. Research Laboratories
8. Research Institutes Focusing on Event Cameras
9. Insights and Recommendations
10. Concluding Remarks