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Last Updated: Apr 25, 2025 | Study Period:
Computer science and language technology research on gesture recognition aims to use mathematical algorithms to analyze human gestures. It belongs to the field of computer vision.
Gestures can come from any body movement or state, although they usually start with the hand or the face. Since hand gestures and faces are both emotions, emotional recognition from them is a focus in the field. Simple gestures can be used by users to interact with or control devices without actually touching them.
Although there have been numerous attempts to read sign language using cameras and computer vision algorithms, gesture recognition techniques are also used to identify and recognize posture, gait, proxemics, and human behaviors.
In comparison to older text user interfaces or even GUIs (graphical user interfaces), which still restrict the majority of input to keyboard and mouse and interact naturally without any mechanical devices, gesture recognition can be seen as a way for computers to start understanding human body language.
A sensor that gathers and transmits data needed to create an image is known as an image sensor or imager. It does this by converting signals, or brief current bursts that carry information, from the light waves' varying attenuation (as they pass through or reflect off objects).
Light or other types of electromagnetic energy might be waves. Both analog and digital electronic imaging devices, such as digital cameras, camera modules, camera phones, optical mice, medical imaging equipment, and night vision equipment including thermal imaging devices, radar, sonar, and other devices, use image sensors. Chemical and analog imaging is frequently being replaced by electronic and digital imaging as technology advances.
The charge-coupled device (CCD) and the active-pixel sensor are the two primary types of electronic image sensors (CMOS sensors). CCDs and CMOS sensors both rely on metal-oxide-semiconductor (MOS) technology, with CMOS sensors using MOSFET (MOS field-effect transistor) amplifiers and CCDs using MOS capacitors.
Vacuum tubes of various types are frequently used in analog sensors for invisible radiation, whereas flat-panel detectors are often used in digital sensors.
The two primary types of digital image sensors are complementary MOS (CMOS) or N-type MOS (NMOS or Live MOS)-fabricated charge-coupled device (CCD) and active-pixel sensor (CMOS sensor).
Both CCD and CMOS sensors are based on MOS technology, with the building blocks of a CCD sensor being MOS capacitors and the building blocks of a CMOS sensor being MOSFET amplifiers.
The Global gesture detection image sensor market accounted for $XX Billion in 2023 and is anticipated to reach $XX Billion by 2030, registering a CAGR of XX% from 2024 to 2030.
Multi-zone ToF gesture detection image sensor for User Presence Detection and Gesture Recognition is Introduced by STMicroelectronics.
Without utilizing a camera or taking pictures, Time-of-Flight (ToF) multi-zone sensors continually scan their range of vision to map the landscape and gather information. The sensors can monitor numerous targets using ToF technology, detecting them simultaneously and quickly determining their X, Y, and Z coordinates as well as their motion.
The combination offers a turnkey solution for user detection, gesture recognition, and intruder warning that is specially created for the PC market when delivered along with a suite of important software algorithms.
The most recent VL53L5CP FlightSense multi-zone sensor has a large 61-degree field of view and can identify multiple targets in 64 (88) zones.
The sensor has Presence Premium PLUS, ST's exclusive third generation of PC application-specific algorithms, which supports its cutting-edge features and innovations while boosting data security.
| Sl no | Topic |
| 1 | Market Segmentation |
| 2 | Scope of the report |
| 3 | Abbreviations |
| 4 | Research Methodology |
| 5 | Executive Summary |
| 6 | Introduction |
| 7 | Insights from Industry stakeholders |
| 8 | Cost breakdown of Product by sub-components and average profit margin |
| 9 | Disruptive innovation in the Industry |
| 10 | Technology trends in the Industry |
| 11 | Consumer trends in the industry |
| 12 | Recent Production Milestones |
| 13 | Component Manufacturing in US, EU and China |
| 14 | COVID-19 impact on overall market |
| 15 | COVID-19 impact on Production of components |
| 16 | COVID-19 impact on Point of sale |
| 17 | Market Segmentation, Dynamics and Forecast by Geography, 2024-2030 |
| 18 | Market Segmentation, Dynamics and Forecast by Product Type, 2024-2030 |
| 19 | Market Segmentation, Dynamics and Forecast by Application, 2024-2030 |
| 20 | Market Segmentation, Dynamics and Forecast by End use, 2024-2030 |
| 21 | Product installation rate by OEM, 2023 |
| 22 | Incline/Decline in Average B-2-B selling price in past 5 years |
| 23 | Competition from substitute products |
| 24 | Gross margin and average profitability of suppliers |
| 25 | New product development in past 12 months |
| 26 | M&A in past 12 months |
| 27 | Growth strategy of leading players |
| 28 | Market share of vendors, 2023 |
| 29 | Company Profiles |
| 30 | Unmet needs and opportunity for new suppliers |
| 31 | Conclusion |
| 32 | Appendix |
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