Walking Recognition
Why Choose Walking Recognition?
Choose this if you want cutting-edge security tech that identifies people by how they walk, not their face. It respects privacy, works in tough conditions, and can spot suspicious behavior without invading personal data.
Turn your CCTV archives into a fingerprint database Identify individuals in crowds from their unique gross motor coordination, without the use of face recognition. Our AI analyzes and recognizes walking patterns, which are just as unique as fingerprints. -Use your existing CCTV system or autonomous drones -Protect personal data by not processing or storing sensitive biometric features Our technology operates effectively under various conditions, including: -Daylight or darkness -When individuals are masked -If the video resolution is too low for face recognition -Supercharge your video analytics with our computer vision expertise and award-winning movement analysis AI. What else can our AI uncover from live video footage? -Detect suspicious behavior, such as signs of nervousness or fleeing in airports, offices, and other public spaces -Flag unauthorized use of NFC and RFID cards or tailgating at access control points -Identify accidents or people in distress in travel hubs, indus-trial or sports facilities -Predict and prevent shoplifting with object and pose recognition Detailed product description Cursor Insight's CCTV gait recognition technology represents a pioneering advancement in the field of surveillance and security. Utilizing advanced algorithms and machine learning models, our deep-tech application analyzes live video footage to discern individuals' unique gross motor coordination, enabling accurate recognition of people based on their distinctive gait patterns and body dimensions. Our system's versatility is a key strength, applicable across various scenarios, from airports and banks to industrial and sports facilities. It can identify people and suspicious behavior, flag unauthorized access attempts, and detect accidents or distress situations. Leveraging existing CCTV archives, our technology transforms them into a valuable fingerprint database, extracting hundreds of movement features without compromising personal data privacy. Additionally, our system can integrate seamlessly with autonomous drone monitoring systems, providing real-time analysis of a person’s unique movement patterns on live video footage. Our technology operates effectively under various conditions, including 24-7 day and night surveillance, situations where individuals are masked, and instances where the resolution of the footage is too low. We strive for innovation, as reflected in our award-winning machine learning technology, praised for its precision in user identification and classification. With a team bringing together over a century of collective experience in machine learning and biometrics, we aim to advance security and surveillance standards, striving for excellence across all facets of our technology. We value our expertise in data preprocessing, utilizing a reverse engineering approach to ensure the accuracy of recreated movements. Our proprietary feature extraction utilizes a universal feature space adapted over 15 years to provide deeper insights into motor program patterns. Our Random Forest-based Multi-Round Screening Method ensures optimal feature selection, identifying crucial subsets for efficient machine learning models. Through our partnership with the Hungarian National Institute of Clinical Neurosciences, we have access to a dedicated Motion Lab equipped with state-of-the-art technology, facilitating efficient insights from laboratory-recorded data. Central to our approach is the protection of personal data and privacy. Unlike traditional biometric systems, we focus solely on analyzing gross motor coordination and body dimensions. We avoid processing or storing sensitive biometric data, such as facial images, to ensure compliance with privacy regulations.
Walking Recognition Introduction
What is Walking Recognition?
Turn your CCTV archives into a fingerprint database Identify individuals in crowds from their unique gross motor coordination, without the use of face recognition. Our AI analyzes and recognizes walking patterns, which are just as unique as fingerprints. -Use your existing CCTV system or autonomous drones -Protect personal data by not processing or storing sensitive biometric features Our technology operates effectively under various conditions, including: -Daylight or darkness -When individuals are masked -If the video resolution is too low for face recognition -Supercharge your video analytics with our computer vision expertise and award-winning movement analysis AI. What else can our AI uncover from live video footage? -Detect suspicious behavior, such as signs of nervousness or fleeing in airports, offices, and other public spaces -Flag unauthorized use of NFC and RFID cards or tailgating at access control points -Identify accidents or people in distress in travel hubs, indus-trial or sports facilities -Predict and prevent shoplifting with object and pose recognition Detailed product description Cursor Insight's CCTV gait recognition technology represents a pioneering advancement in the field of surveillance and security. Utilizing advanced algorithms and machine learning models, our deep-tech application analyzes live video footage to discern individuals' unique gross motor coordination, enabling accurate recognition of people based on their distinctive gait patterns and body dimensions. Our system's versatility is a key strength, applicable across various scenarios, from airports and banks to industrial and sports facilities. It can identify people and suspicious behavior, flag unauthorized access attempts, and detect accidents or distress situations. Leveraging existing CCTV archives, our technology transforms them into a valuable fingerprint database, extracting hundreds of movement features without compromising personal data privacy. Additionally, our system can integrate seamlessly with autonomous drone monitoring systems, providing real-time analysis of a person’s unique movement patterns on live video footage. Our technology operates effectively under various conditions, including 24-7 day and night surveillance, situations where individuals are masked, and instances where the resolution of the footage is too low. We strive for innovation, as reflected in our award-winning machine learning technology, praised for its precision in user identification and classification. With a team bringing together over a century of collective experience in machine learning and biometrics, we aim to advance security and surveillance standards, striving for excellence across all facets of our technology. We value our expertise in data preprocessing, utilizing a reverse engineering approach to ensure the accuracy of recreated movements. Our proprietary feature extraction utilizes a universal feature space adapted over 15 years to provide deeper insights into motor program patterns. Our Random Forest-based Multi-Round Screening Method ensures optimal feature selection, identifying crucial subsets for efficient machine learning models. Through our partnership with the Hungarian National Institute of Clinical Neurosciences, we have access to a dedicated Motion Lab equipped with state-of-the-art technology, facilitating efficient insights from laboratory-recorded data. Central to our approach is the protection of personal data and privacy. Unlike traditional biometric systems, we focus solely on analyzing gross motor coordination and body dimensions. We avoid processing or storing sensitive biometric data, such as facial images, to ensure compliance with privacy regulations.